Text-to-Speech (TTS) is the task of generating natural sounding speech given text input. TTS models can be extended to have a single model that generates speech for multiple speakers and multiple languages. Build a Text-to-Speech App | AI Engineer Here are terms definitions related to text-to-speech (TTS) models: • Text-to-speech (TTS): The task of converting text into speech. TTS models are trained on large datasets of text and speech, and they can generate speech in a variety of languages and voices. • Natural sounding speech: Speech that sounds like it was produced by a human. TTS models have made significant progress in recent years in generating natural-sounding speech. • Speaker: The person or character who is speaking. TTS models can be trained to generate speech for multiple speakers, with different voices and accents. • Language: The system of communication used by a particular community or nation. TTS models can be trained to generate speech in multiple languages. • Multi-speaker TTS model: A TTS model that can generate speech for multiple speakers, with different voices and accents. • Multi-lingual TTS model: A TTS model that can generate speech in multiple languages. Text-to-speech (TTS) models are a type of artificial intelligence (AI) that can convert text into natural-sounding speech. TTS models are trained on large datasets of text and speech, and they can generate speech in a variety of languages and voices. TTS models are used in a variety of applications, including: • Accessibility: TTS models can be used to help people who are blind or have low vision access information and services. For example, a TTS model can be used to read text aloud from a website or to provide audio descriptions of videos. • Education: TTS models can be used to create educational materials that are more engaging and accessible to students. For example, a TTS model can be used to create audio versions of textbooks or to generate interactive learning experiences. • Customer service: TTS models can be used to create customer service chatbots that can provide assistance to customers in a more natural and efficient way. • Entertainment: TTS models can be used to create audiobooks, podcasts, and other forms of entertainment. • Productivity: TTS models can be used to create tools that can help people to be more productive, such as tools that can read emails aloud or generate transcripts of meetings. Here are some specific examples of how TTS is used in the use cases you mentioned: • Voice assistants: TTS models are used to create voice assistants on smart devices, such as Amazon Alexa and Google Assistant. These voice assistants can be used to control smart home devices, get information, and perform a variety of other tasks. • Announcement systems: TTS models are widely used in airport and public transportation announcement systems. These systems use TTS to convert text announcements into speech, which can be heard by passengers. • Navigation systems: TTS models are used in navigation systems to provide spoken directions to drivers and pedestrians. • E-learning: TTS models are used in e-learning platforms to create audio versions of course materials. This can make learning more accessible to students who have visual impairments or who learn better by listening. • Gaming: TTS models are used in video games to create voice acting for characters and to provide spoken feedback to players. TTS models are still under development, but they have already made significant progress in recent years. TTS models can now generate speech that is very close to human quality, and they are becoming increasingly affordable and accessible. Here are some examples of popular TTS models: • Google Cloud Text-to-Speech • Amazon Polly • IBM Watson Text-to-Speech • Microsoft Azure Text-to-Speech • Coqui TTS TTS models are a powerful tool that can be used to improve the accessibility, engagement, and efficiency of a wide range of applications. How to Build a Text-to-Speech App with a Custom Voice To build custom modules for a custom text-to-speech (TTS) voice, you will need to: 1. Collect a dataset of the individual's voice. This dataset should include a variety of sentences and phrases, spoken in different tones and contexts. 2. Choose a TTS model. There are many different TTS models available, both open source and commercial. Choose a model that is well-suited for your specific needs, such as the type of voice you want to create and the languages you need to support. 3. Train the TTS model on the dataset of the individual's voice. This process can be time-consuming and computationally expensive, depending on the size and complexity of the dataset and the TTS model you are using. 4. Evaluate the trained TTS model. Once the model is trained, you need to evaluate its performance on a held-out test dataset. This will help you to identify any areas where the model needs improvement. 5. Create modules for the trained TTS model. Once you are satisfied with the model's performance, you need to create modules that can be used to generate speech from text. These modules can be implemented in a variety of ways, depending on the programming language and platform you are using. Here are some additional tips for building custom modules for a custom TTS voice: • Use a high-quality dataset of the individual's voice. The larger and more diverse the dataset, the better the TTS model will be able to learn the individual's voice patterns. • Choose a TTS model that is well-suited for your specific needs. For example, if you need to create a voice that can speak multiple languages, you will need to choose a TTS model that supports those languages. • Train the TTS model on a powerful computer. Training a TTS model can be computationally expensive, so it is important to use a computer that has enough processing power and memory. • Evaluate the trained TTS model carefully. Listen to the generated speech and compare it to the individual's voice. Make sure that the generated speech sounds natural and that it accurately reflects the individual's voice patterns. • Create modules for the trained TTS model that are easy to use. For example, you could create a library that can be used to generate speech from text in different programming languages. Once you have created modules for the trained TTS model, you can use them to generate custom text-to-speech voices for a variety of applications. Here are some examples of how you can use custom modules for a custom TTS voice: • Create a custom voice for your voice assistant. You could use a custom TTS voice to create a voice assistant that sounds like you. This could be useful for a variety of tasks, such as controlling your smart home devices or getting directions to your destination. • Create a custom voice for your audiobook or podcast. You could use a custom TTS voice to create an audiobook or podcast that sounds like you. This could be a great way to share your stories with the world. • Create a custom voice for your video game character. You could use a custom TTS voice to create a video game character that sounds like you. This could help to create a more immersive and engaging gaming experience. These are just a few examples of how you can use custom modules for a custom TTS voice. As TTS technology continues to improve, we can expect to see even more innovative and creative uses of custom TTS voices in the future. Open Source text-to-speech (TTS) models There are many open source text-to-speech (TTS) models available. Here are a few of the most popular: • Coqui AI TTS: This TTS model is trained on a large dataset of text and audio, and it can generate speech in a variety of languages. • Tacotron2: This TTS model is known for its high-quality speech output. It is trained on a large dataset of text and audio, and it can generate speech in a variety of languages. • WaveNet: This TTS model is known for its ability to generate speech that sounds very natural. It is trained on a large dataset of text and audio, and it can generate speech in a variety of languages. • LibriTTS: This TTS model is trained on a large dataset of audiobooks, and it can generate speech in a variety of languages. • Merlin: This TTS model is trained on a large dataset of text and audio, and it can generate speech in a variety of languages. These are just a few examples of the many open source TTS models that are available. When choosing a TTS model, it is important to consider your specific needs, such as the type of voice you want to create and the languages you need to support. Here are some of the benefits of using open source TTS models: • Cost: Open source TTS models are typically free to use, which can save you a lot of money if you are developing a commercial product. • Customization: Open source TTS models can be customized to meet your specific needs. For example, you can train an open source TTS model on a dataset of your own voice to create a voice that is truly unique to you. • Community support: Open source TTS models are often supported by a large community of developers who can help you with any problems you may encounter. If you are looking for an open source TTS model, I recommend checking out the websites of the companies and projects listed above. Explore more TTS models at Hugging Face: https://huggingface.co/tasks/text-to-speech(https://huggingface.co/tasks/text-to-speech) Codersarts AI: TTS-Based Services for Custom App Development Codersarts AI offers a variety of TTS-based services, including: • App development: We can help you develop custom TTS-based apps for your specific needs. • Model training and deployment: We can help you train and deploy custom TTS models that can generate speech that sounds like you or your brand. • API integration: We can help you integrate TTS APIs into your existing applications. • PoCs, MVPs, and other demanding services: We can help you build PoCs, MVPs, and other demanding TTS-based solutions. If you are interested in learning more about our TTS-based services, please contact us for a free consultation. React out to us via contact@codersarts.com(mailto:contact@codersarts.com) Client success story for a TTS-based app helped by Codersarts Client: A large e-commerce company Challenge: The company wanted to develop a TTS-based app that would allow customers to listen to product descriptions and customer reviews while shopping. Solution: Codersarts AI developed a custom TTS-based app for the company that uses a state-of-the-art TTS model to generate natural-sounding speech. The app also includes a variety of features, such as the ability to save product descriptions and customer reviews for later listening, and the ability to adjust the speech rate and pitch. Results: The app has been well-received by customers, and it has helped to increase sales and customer satisfaction. The company has also seen a reduction in the number of customer support tickets, as customers are now able to find the information they need more easily. Client: A small educational startup Challenge: The startup wanted to develop a TTS-based app that would help students with dyslexia learn to read. Solution: Codersarts AI developed a custom TTS-based app for the startup that uses a special TTS model that is designed to generate speech that is easy for students with dyslexia to understand. The app also includes a variety of features, such as the ability to highlight words and phrases as they are spoken, and the ability to adjust the volume and pitch of the speech. Results: The app has been very helpful for students with dyslexia, and it has helped to improve their reading skills. The startup has also seen a significant increase in demand for its app, and it is now used by schools and families all over the world. These are just a few examples of how Codersarts AI has helped clients to develop anddeploy successful TTS-based apps. Codersarts AI has a team of experienced AI engineers who can help you to create a custom TTS-based solution that meets your specific needs.

Data engineers, data scientists, and machine learning engineers are all important roles in the field of data science. They all work with data, but they have different skills and responsibilities. Data engineers vs Data scientists vs ML engineers Data engineers are responsible for building and maintaining the infrastructure and systems that support data collection, storage, processing, and analysis. They work with large data sets and develop data pipelines to move data from source systems to data warehouses, data lakes, and other data storage and processing systems. They also develop and maintain data APIs, ETL processes, and data integration systems. Key Responsibilities: • Design & Maintenance: Create and maintain optimal data pipeline architectures. • Data Collection & Storage: Set up and manage big data tools and platforms, ensuring data is collected, stored, and processed efficiently. • Data Cleaning: Clean and preprocess data to ensure its reliability and readiness for analysis. • Collaboration: Work closely with data scientists and ML engineers to provide the necessary data and infrastructure. Skills: • Strong programming skills (e.g., Python, Java, Scala). • Expertise in SQL and database technologies (both relational and NoSQL). • Familiarity with big data tools (e.g., Hadoop, Spark). • Cloud platforms knowledge (e.g., AWS, Google Cloud, Azure). • ETL tools proficiency. Role in the Data Ecosystem: • The backbone, ensuring the infrastructure is in place to gather, store, and make data accessible for analysis and model training. Data scientists are responsible for collecting, analyzing, and interpreting data to solve problems. They use machine learning and other statistical methods to extract insights from data. Data scientists work in a variety of industries, including healthcare, finance, and technology. Key Responsibilities: • Data Exploration: Dive deep into data to discover insights and patterns. • Hypothesis Testing: Formulate and test hypotheses using statistical methods. • Model Development: Build basic predictive models to solve business problems. • Data Visualization: Create visualizations to represent findings and insights. • Collaboration: Work alongside business teams to understand problems and provide data-driven solutions. Skills: • Strong statistical and analytical skills. • Proficiency in programming (commonly Python or R). • Familiarity with ML libraries (e.g., scikit-learn, TensorFlow). • Expertise in data visualization tools (e.g., Matplotlib, Seaborn, Tableau). • SQL knowledge. Role in the Data Ecosystem: • The bridge between raw data and actionable insights, turning data into information that can guide decision-making. Machine learning engineers are responsible for building and deploying machine learning models. They work with data scientists to understand the problem that the model needs to solve and then develop and train a model to solve that problem. Machine learning engineers also work to deploy machine learning models to production so that they can be used to make predictions on new data. Key Responsibilities: • Model Building: Develop advanced ML and AI models, going beyond what typical data scientists build. • Model Optimization: Fine-tune models for performance and scalability. • Deployment: Ensure ML models are deployable into production environments. • Maintenance: Monitor and update models in real-world settings. • Collaboration: Work closely with data engineers and data scientists to integrate models into data pipelines and applications. Skills: • Deep knowledge of ML algorithms and frameworks (e.g., TensorFlow, PyTorch). • Strong programming skills (e.g., Python, C++). • Knowledge of cloud platforms and deployment tools. • Familiarity with big data tools and architectures. • DevOps skills for ML (MLOps), ensuring smooth deployment and scalability. Role in the Data Ecosystem: • The specialist in turning data into functioning AI models, ensuring they are optimized, deployable, and maintainable. Here is a table that summarizes the key differences between data engineers, data scientists, and machine learning engineers: key differences between data engineers, data scientists, and machine learning engineers: In Summary: • Data Engineers focus on building infrastructure for data generation, collection, and storage. • Data Scientists explore this data, derive insights, and create basic models. • Machine Learning Engineers specialize in building and deploying complex models. While there's overlap, each role has distinct responsibilities in the data-to-decision pipeline. Collaboration between these roles is essential to create data-driven solutions effectively. Which role is right for you depends on your skills and interests. If you are interested in building and maintaining data infrastructure, then a data engineer role may be a good fit for you. If you are interested in collecting, analyzing, and interpreting data to solve problems, then a data scientist role may be a good fit for you. If you are interested in building and deploying machine learning models, then a machine learning engineer role may be a good fit for you. Here is a real business example of how data engineers, data scientists, and machine learning engineers work together: A retail company wants to use machine learning to predict which customers are most likely to churn. The data engineer builds a data pipeline to move customer data from the company's CRM system to a data warehouse. The data scientist then cleans and analyzes the data to identify patterns that can be used to predict customer churn. The machine learning engineer then builds and trains a machine learning model to predict customer churn. The model is then deployed to production so that the company can use it to identify customers who are at risk of churning and take steps to retain them. Here is a more detailed breakdown of how each role is involved in this project: Data engineer: • Builds a data pipeline to move customer data from the company's CRM system to a data warehouse. • Develops data quality checks to ensure that the data is accurate and reliable. • Transforms the data into a format that can be used by the data scientist. Data scientist: • Cleans and analyzes the customer data to identify patterns that can be used to predict customer churn. • Uses machine learning and other statistical methods to develop a model to predict customer churn. • Evaluates the performance of the model to ensure that it is accurate and reliable. Machine learning engineer: • Deploys the machine learning model to production so that the company can use it to identify customers who are at risk of churning. • Monitors the performance of the model in production and makes adjustments as needed. • Works with the data scientist to improve the model over time. This is just one example of how data engineers, data scientists, and machine learning engineers work together to solve real-world business problems. They all play important roles in the development and deployment of machine learning systems. Salary: Data Engineer, Data Scientist, and Machine Learning Engineer The salary for data engineers, data scientists, and machine learning engineers can vary depending on a number of factors, including experience, skills, location, and the company they work for. However, in general, all three roles are well-paid. According to Glassdoor, the average annual salary for data engineers in the United States is $103,923, for data scientists is $114,596, and for machine learning engineers is $125,040. The salary range for all three roles is typically between $77,000 and $142,000. However, the highest-paid professionals in each role can earn significantly more. For example, the average annual salary for a data engineer at Google is $136,000, for a data scientist at Google is $143,000, and for a machine learning engineer at Google is $152,000. Here are some factors that can affect a data engineer's salary: • Experience: Data engineers with more experience typically earn higher salaries. • Skills: Data engineers with specialized skills, such as experience with big data technologies or machine learning, typically earn higher salaries. • Location: Data engineers in high-cost areas, such as San Francisco and New York City, typically earn higher salaries. • Company: Data engineers who work for large tech companies typically earn higher salaries than those who work for smaller companies. If you are interested in a career as a data engineer, there are a few things you can do to increase your chances of earning a high salary. First, make sure to get a strong education in computer science and mathematics. Second, gain experience with big data technologies and machine learning. Third, consider working for a large tech company. Elevate Your Data Career: Tailored Support for Data Engineers, Data Scientists, and ML Engineers at Codersarts 1. For Data Engineers: • Dive deeper into the world of data infrastructure! At Codersarts, we offer dedicated support for Data Engineers, from hands-on project assistance to advanced training. Shape the future of data flow with us. 3. For Data Scientists: • Unravel the mysteries of data with Codersarts! We're here to bolster your journey as a Data Scientist, providing you with expert guidance, advanced coursework, and real-world project support. 5. For ML Engineers: • Push the boundaries of machine learning with Codersarts! Whether you're building neural networks or refining algorithms, we provide specialized training, project assistance, and job support for ML Engineers. Navigating the intersections of Data Engineering, Data Science, and Machine Learning? Codersarts is here to guide you. Offering tailored training, project support, and expert guidance for all three roles. Connect today at contact@codersarts.com(mailto:contact@codersarts.com)

A data engineer is a professional responsible for preparing "big data" for analytical or operational uses. They are the architects, builders, and maintainers of the data pipeline, ensuring that data flows smoothly from diverse sources to databases and data warehouses. Data Engineer: Skills and Responsibilities Data engineers are responsible for designing, building, and maintaining the infrastructure and systems that support data collection, storage, processing, and analysis. They work with large data sets and develop data pipelines to move data from source systems to data warehouses, data lakes, and other data storage and processing systems. They also develop and maintain data APIs, ETL processes, and data integration systems. Data engineers play a critical role in helping organizations to collect, manage, and analyze their data. They are in high demand as businesses increasingly rely on data to make informed decisions. Responsibilities of a Data Engineer: • Design, build, and maintain data pipelines to move data from source systems to data warehouses, data lakes, and other data storage and processing systems. • Develop and maintain data APIs, ETL processes, and data integration systems. • Work with other data professionals, such as data scientists and data analysts, to ensure that the data infrastructure meets the needs of the organization. • Monitor and troubleshoot data systems to ensure that they are running smoothly and efficiently. • Implement security measures to protect data from unauthorized access. • Stay up-to-date on the latest data technologies and best practices. Skills a Data Engineer Should Possess: • Technical Prowess: Familiarity with programming languages like Python, Java, or Scala. • Database Mastery: Deep knowledge of relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra). • Big Data Expertise: Proficiency with big data tools like Hadoop, Spark, and Kafka. • Cloud Savvy: Experience with cloud platforms like AWS, Google Cloud, or Azure. • Problem-Solving Skills: Ability to troubleshoot and address challenges in data flow and processing. Career Path for Data Engineers: Data engineers can typically expect to advance to senior data engineer positions, and may also move into management or leadership roles. With the increasing demand for data engineers, there are also many opportunities for data engineers to start their own businesses or consultancies. The career path for Data Engineers can be both diverse and rewarding. Here's a detailed look at the progression, opportunities, and potential specializations available: 1. Educational Background: • Bachelor's Degree: Most data engineers begin with a bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. • Specialized Courses: Taking courses or certifications in databases, big data technologies, and cloud platforms can be beneficial. 2. Entry-Level Positions: a. Data Analyst: • Analyzing data to identify patterns. • Gaining familiarity with data tools and SQL. b. Junior Data Engineer: • Assisting in building and maintaining data pipelines. • Working under the guidance of senior data engineers. 3. Mid-Level Positions: a. Data Engineer: • Designing, constructing, installing, and maintaining large-scale processing systems. • Managing and optimizing databases. • Developing ETL processes. b. Database Administrator: • Ensuring that databases are available, performant, and secure. • Managing database access. c. Big Data Engineer: • Specializing in big data technologies like Hadoop and Spark. • Working on more complex, large-scale data processing tasks. 4. Senior-Level Positions: a. Senior Data Engineer: • Leading data engineering teams. • Making architectural decisions. • Collaborating closely with data scientists and business stakeholders. b. Data Architect: • Designing the structure and layout of data systems. • Defining how data is stored, accessed, and processed across the organization. 5. Specializations and Niches: a. Machine Learning Engineer: • Transitioning to developing algorithms and predictive models. • Requires strong knowledge of machine learning libraries and algorithms. b. Cloud Data Engineer: • Specializing in cloud-based data storage and processing systems, such as AWS, Google Cloud, or Azure. c. Streaming Data Engineer: • Focusing on real-time data processing technologies like Kafka or Storm. 6. Leadership and Management Roles: a. Lead Data Engineer/Team Lead: • Managing and guiding data engineering teams. • Collaborating with other department leads. b. Director of Data Engineering: • Overseeing multiple data engineering teams. • Setting strategic goals and ensuring alignment with business objectives. c. Chief Data Officer (CDO): • Part of the executive team, responsible for the entire data strategy of the organization. Hierarchy in the AI Ecosystem: • AI/ML Strategist or Researcher: • The visionary who understands the business or scientific needs and conceptualizes how AI/ML can be utilized. They set the direction and goals. • Data Architect: • Designs the overall structure of the data ecosystem. Determines how data will be stored, accessed, and integrated across platforms. • Data Engineer: • Implements the vision of the data architect. Ensures data is collected, stored, cleaned, and made accessible for AI/ML applications. (This is the bridge between raw data and usable data for ML models.) • Machine Learning Engineer: • Takes the clean data and develops ML models. They choose appropriate algorithms, train models, and refine their performance. • Data Scientist: • Explores the data to gain insights and often collaborates with ML engineers in model development. They might also be involved in more statistically rigorous analyses and experimental design. • AI/ML Ops or DevOps for AI: • Ensures that the ML models can be deployed into production environments efficiently. They handle scaling, monitoring, and updating models in real-world settings. • AI Product Manager: • Manages the AI product lifecycle, ensuring that AI applications are aligned with business goals and meet user needs. Data engineer salary The salary of a data engineer can vary depending on their experience, skills, location, and the company they work for. However, in general, data engineers are well-paid professionals. Here are some reference links of websites that provide information on data engineer salaries: • Glassdoor: https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm(https://www.glassdoor.com/Salaries/data-engineer-salary-SRCH_KO0,13.htm) • Indeed: https://www.indeed.com/cmp/Indeed/salaries/Data-Engineer(https://www.indeed.com/cmp/Indeed/salaries/Data-Engineer) • PayScale: https://www.payscale.com/research/US/Job=Data_Engineer/Salary(https://www.payscale.com/research/US/Job=Data_Engineer/Salary) • Salary.com: https://www.salary.com/research/salary/listing/data-engineer-salary(https://www.salary.com/research/salary/listing/data-engineer-salary) • Levels.fyi: https://www.levels.fyi/t/software-engineer/focus/data(https://www.levels.fyi/t/software-engineer/focus/data) These websites collect salary data from real employees and provide users with information on average salaries, salary ranges, and salary trends. They also allow users to filter the data by experience, skills, location, and company. You can also use these websites to compare your salary to other data engineers in your field. This can help you to determine if you are being paid fairly and to negotiate a higher salary if you are not. Guide to Building a Comprehensive Data Engineering Portfolio Building a portfolio for data engineering projects involves showcasing a range of skills, from data ingestion and ETL processes to database design and big data technologies. A strong portfolio can significantly enhance your visibility to employers or clients. Here's a step-by-step guide to build a comprehensive portfolio: 1. Define Your Skillset: List out the skills you want to showcase, such as: • Database management (SQL, NoSQL). • ETL processes. • Big data tools (Hadoop, Spark). • Cloud platforms (AWS, Google Cloud, Azure). • Data pipelines and workflows. 2. Project Ideas: a. Data Ingestion & ETL: • Project: Set up a process to scrape web data (e.g., stock prices, weather data) and store it in a database. • Skills Demonstrated: Web scraping, ETL processes, database management. b. Database Design: • Project: Design a relational database for an e-commerce platform or any domain you're interested in. • Skills Demonstrated: Database design, SQL, normalization. c. Big Data Processing: • Project: Use a dataset from Kaggle and process it using Spark, showcasing how you can handle big data. • Skills Demonstrated: Spark, big data processing. d. Data Pipeline Creation: • Project: Build a real-time data pipeline using tools like Kafka or Airflow, taking a data source and feeding it into a visualization tool or dashboard. • Skills Demonstrated: Real-time processing, streaming data, data visualization. e. Cloud-Based Project: • Project: Migrate a local database to a cloud platform, setting up a data warehouse using tools like AWS Redshift or Google BigQuery. • Skills Demonstrated: Cloud platforms, data warehousing. f. Data Lake Implementation: • Project: Build a data lake using tools like AWS S3 or Hadoop HDFS, showcasing the ingestion, storage, and retrieval of data. • Skills Demonstrated: Data lakes, big data storage. Your portfolio is a dynamic representation of your skills and expertise in data engineering. By showcasing a diverse range of projects and regularly updating it, you'll position yourself as a knowledgeable and proactive data engineer, attracting potential employers or clients. Conclusion Data engineers play a critical role in helping organizations to collect, manage, and analyze their data. They are in high demand as businesses increasingly rely on data to make informed decisions. If you are interested in a career in data engineering, there are many resources available to help you learn the skills and experience you need to get started. Ready to elevate your Data Engineering skills? At Codersarts, we provide tailored work and job support, hands-on project assistance, and specialized course training for Data Engineers. Unlock your potential and stay ahead in the industry. Reach out to us now at contact@codersarts.com!(mailto:contact@codersarts.com)

A vector database is a type of database that stores data as high-dimensional vectors. Vectors are mathematical representations of objects or entities, and they can be used to represent a wide variety of data, such as images, text, and audio. Vector databases are designed to efficiently store and retrieve data that is similar to each other. Illustration of a digital storage system labeled 'Vector Databases' Uses of vector databases: • Semantic search: Vector databases can be used to power semantic search engines, which are able to understand the meaning of queries and return results that are relevant to the user's intent. • Recommendation systems: Vector databases can be used to power recommendation systems, which are able to recommend items to users based on their past behavior or preferences. • Fraud detection: Vector databases can be used to detect fraud by identifying patterns in data that are indicative of fraudulent activity. • Anomaly detection: Vector databases can be used to detect anomalies in data by identifying data points that are significantly different from the rest of the data. • Image and audio similarity search: Vector databases can be used to find images or audio recordings that are similar to a given query image or audio recording. How Vector Databases Work Vector databases store data as vectors, which are mathematical objects that can be represented as arrays of numbers. Each number in a vector represents a feature of the entity or concept being represented. For example, a vector representing an image might contain features such as the average pixel intensity, the presence of certain colors, or the distribution of edges. To find similar vectors, vector databases use a variety of techniques, such as cosine similarity and Euclidean distance. Cosine similarity measures the angle between two vectors, while Euclidean distance measures the straight-line distance between two vectors. Vectors that are more similar will have a smaller angle or a shorter distance between them. Top vector databases to learn as a developer: • Milvus: Milvus is an open-source vector database that is designed for scalability and performance.  • Pinecone: Pinecone is a cloud-based vector database that is designed for ease of use.  • Faiss: Faiss is a library for efficient similarity search on CPU and GPUs.  • Annoy: Annoy is a library for approximate nearest neighbor search.  • NMSLIB: NMSLIB is a library for similarity search on large datasets.  Learning about vector databases can be a valuable skill for developers who are working on projects that involve semantic search, recommendation systems, fraud detection, anomaly detection, or image and audio similarity search. Additional resources for learning about vector databases: • Vector Databases: The Definitive Guide by Yannis Katsis and Aristidis Protopapadakis  • Vector Search: The Secret Sauce of Modern Applications by David Arthur and Andrey Maletz  • Vector Databases for Practitioners by Alexey Boyarsky, Dmitry Konovalov, Ilya Ovodov, and Vladimir Yavorskiy  The Growing Importance of Vector Databases for AI Engineers Vector databases are a valuable skill for AI engineers to learn. This is because vector databases are well-suited for storing and retrieving high-dimensional data, which is a common type of data in AI applications. For example, vector databases can be used to store image data, audio data, and natural language processing (NLP) data. In addition, vector databases are often used to power similarity search applications. Similarity search is a type of query that finds data points that are similar to a given query point. This type of query is common in AI applications such as image retrieval, recommender systems, and fraud detection. By learning about vector databases, AI engineers can gain the skills they need to build and maintain AI applications that involve high-dimensional data and similarity search. In addition, learning about vector databases can help AI engineers to better understand the underlying data structures and algorithms that are used in AI applications. Here are some specific examples of how AI engineers can use vector databases: • Image retrieval: Vector databases can be used to store and retrieve images based on their visual similarity. This can be used to build applications such as image search engines and image recommendation systems. • Natural language processing (NLP): Vector databases can be used to store and retrieve word embeddings, which are numerical representations of words. This can be used to build applications such as machine translation, text summarization, and question answering. • Recommender systems: Vector databases can be used to store and retrieve user profiles and product information. This can be used to build recommender systems that suggest products to users based on their past behavior or preferences. • Fraud detection: Vector databases can be used to store and retrieve transaction data. This can be used to build fraud detection systems that identify patterns in data that are indicative of fraudulent activity. Overall, vector databases are a valuable skill for AI engineers to learn. By learning about vector databases, AI engineers can gain the skills they need to build and maintain AI applications that involve high-dimensional data and similarity search.

Developing a Voice Sentiment Analysis Model Using Generative AI Goal: To create a model that can accurately capture customer emotions from their voices during phone conversations. Tasks: • Research and evaluate different generative AI techniques for voice sentiment analysis. • Collect and annotate a dataset of voice recordings with corresponding sentiment labels. • Train and evaluate a voice sentiment analysis model using the annotated dataset. • Integrate the voice sentiment analysis model into the CX platform. Benefits: • Gain experience with generative AI techniques for voice sentiment analysis. • Develop a valuable skill that is in high demand. • Contribute to the development of a cutting-edge CX platform. Potential impact: • Improved customer satisfaction through better understanding of customer emotions. • More effective customer service through targeted interventions. • Increased sales and revenue through more personalized interactions. Alignment with client job description: • Demonstrates experience with voice sentiment analysis. • Shows ability to apply generative AI techniques to solve real-world problems. • Provides evidence of a commitment to developing cutting-edge solutions. By working on this project, you will be able to gain the skills and experience that are necessary to be successful in the role of AI developer. You will also be able to demonstrate your ability to apply generative AI techniques to solve real-world problems. This will make you a strong candidate for the AI developer position at the client company. CX platform stands for customer experience platform. It is a technology solution that helps businesses manage and improve the customer experience across all touchpoints. CX platforms typically include a variety of tools and features for collecting, analyzing, and acting on customer feedback. Some of the benefits of using a CX platform include: - Improved customer satisfaction: By understanding customer needs and expectations, businesses can improve the overall customer experience. - Increased customer loyalty: By providing a positive customer experience, businesses can encourage customers to return and do business with them again. - Reduced customer churn: By identifying and addressing customer pain points, businesses can reduce the number of customers who leave for a competitor. - Increased sales and revenue: By providing a positive customer experience, businesses can increase sales and revenue. CX platforms can be used by businesses of all sizes in a variety of industries. Some common examples of CX platforms include: - Salesforce Service Cloud - Oracle CX Cloud - Microsoft Dynamics 365 Customer Service - Zendesk - Qualtrics If you are interested in learning more about CX platforms, you can visit the websites of the vendors listed above. Demand for voice sentiment analysis models using generative AI The demand for voice sentiment analysis models using generative AI is expected to grow significantly in the coming years. This is due to a number of factors, including: • The increasing use of voice-based communication channels: Voice is becoming an increasingly popular way for customers to interact with businesses. For example, the use of voice assistants such as Siri, Alexa, and Google Assistant is growing rapidly. As a result, businesses are looking for ways to understand and respond to customer sentiment expressed through voice. • The limitations of traditional sentiment analysis methods: Traditional sentiment analysis methods, such as those based on text analysis, are not always effective in capturing the nuances of human emotion. Generative AI models can be used to overcome these limitations by learning to identify patterns in vocal tone, intonation, and other features of speech that are indicative of emotion. • The potential benefits of voice sentiment analysis: Voice sentiment analysis can provide businesses with a number of benefits, such as: • Improved customer understanding: By understanding customer sentiment, businesses can better understand customer needs and expectations. • Enhanced customer service: By identifying and addressing negative customer sentiment, businesses can improve the overall customer experience. • Increased sales and revenue: By understanding customer sentiment, businesses can tailor their marketing and sales efforts to be more effective. As a result of these factors, the demand for voice sentiment analysis models using generative AI is expected to grow significantly in the coming years. Businesses that are able to develop and deploy these models will be well-positioned to gain a competitive advantage. In addition to the above, the demand for voice sentiment analysis models using generative AI is also being driven by the following trends: • The growth of the contact center industry: The contact center industry is a multi-billion dollar industry that is expected to continue to grow in the coming years. As a result, there is a growing demand for solutions that can help contact centers improve their efficiency and effectiveness. Voice sentiment analysis models can be used to help contact centers identify and address customer needs more quickly and effectively. • The increasing use of artificial intelligence (AI) in customer service: AI is being used in a growing number of customer service applications. For example, AI-powered chatbots are being used to provide customers with 24/7 support. Voice sentiment analysis models can be used to improve the effectiveness of these chatbots by helping them to understand customer sentiment. • The growing importance of customer experience (CX): Customer experience is becoming increasingly important to businesses. As a result, businesses are looking for ways to improve the customer experience. Voice sentiment analysis models can be used to help businesses identify and address customer pain points. Overall, the demand for voice sentiment analysis models using generative AI is expected to grow significantly in the coming years. Businesses that are able to develop and deploy these models will be well-positioned to gain a competitive advantage. Implementation Guide for Developing a Voice Sentiment Analysis Model Using Generative AI 1. Collect and annotate a dataset of voice recordings with corresponding sentiment labels. • The dataset should include a variety of voices and emotions. • The sentiment labels can be obtained through manual annotation or by using a crowdsourcing platform. 2. Preprocess the voice recordings. • This may include tasks such as noise reduction, silence removal, and speaker normalization. 3. Extract features from the voice recordings. • This may include features such as pitch, formants, and mel-frequency cepstral coefficients (MFCCs). 4. Train a generative AI model on the extracted features and sentiment labels. • This may involve using a variety of techniques such as deep learning, reinforcement learning, or adversarial learning. 5. Evaluate the performance of the generative AI model on a held-out test set. • This will help to determine the accuracy of the model. 6. Deploy the generative AI model to a production environment. • This may involve integrating the model into a CX platform or other application. Additional considerations: • The choice of generative AI technique will depend on the specific requirements of the application. • The size and quality of the dataset will have a significant impact on the performance of the model. • The model may need to be fine-tuned for specific use cases. Example generative AI techniques for voice sentiment analysis: • Variational autoencoders (VAEs) • Generative adversarial networks (GANs) • Deep belief networks (DBNs) • Recurrent neural networks (RNNs) Benefits of using generative AI for voice sentiment analysis: • Generative AI models can learn to capture the nuances of human emotion that are not easily captured by traditional methods. • Generative AI models can be used to generate synthetic data, which can be used to augment the training dataset. • Generative AI models can be used to create personalized models for individual users. Challenges of using generative AI for voice sentiment analysis: • Generative AI models can be computationally expensive to train. • Generative AI models can be difficult to interpret. • Generative AI models can be biased, depending on the data they are trained on. Overall, generative AI is a promising approach for voice sentiment analysis. By following the steps outlined in this guide, you can develop a voice sentiment analysis model that can be used to improve the customer experience.

A data analyst is a professional who collects, cleans, and analyzes data to identify trends and patterns. They use this information to help businesses make better decisions. Data analysts typically work in a variety of industries, including technology, finance, healthcare, and retail. Data Analytics Services Responsibilities of a data analyst Data analysts play a crucial role in interpreting data to provide actionable insights for businesses and organizations. Here are the primary responsibilities of a data analyst: 1. Data Collection: • Gather data from primary and secondary sources, ensuring its accuracy and relevance. 3. Data Cleaning and Preprocessing: • Identify, correct, or remove corrupt, inaccurate, or irrelevant data. • Convert raw data into a structured and usable format. 6. Statistical Analysis: • Apply statistical methods to analyze data and generate insights. • Identify trends, correlations, and patterns within datasets. 9. Data Visualization: • Create clear and compelling visual representations of the analysis, such as charts, graphs, and dashboards. • Use visualization tools like Tableau, Power BI, Matplotlib, or Seaborn to convey findings effectively. 12. Reporting: • Summarize and present findings to stakeholders in a clear and understandable manner. • Develop regular reports and dashboards for ongoing data tracking and monitoring. 15. Collaboration: • Work closely with other departments, such as marketing, finance, and operations, to understand their data needs and provide relevant insights. • Coordinate with data engineers and IT staff to access and manipulate data sources as needed. 18. Database Querying: • Use SQL or other querying languages to extract data from relational databases. • Ensure that the data extraction process is efficient and meets analytical needs. 21. Continuous Learning: • Stay updated with the latest analytical methods, tools, and best practices. • Attend workshops, webinars, and courses to enhance analytical skills. 24. Problem-Solving: • Address business challenges and questions by leveraging data-driven insights. 26. Ensuring Data Integrity and Compliance: • Adhere to data privacy regulations and ensure that data handling and storage practices are compliant with organizational policies and legal standards. 1. Business Acumen: • Understand the industry, business operations, and objectives to ensure that data analysis aligns with and supports business goals. 1. Data Warehousing and ETL Processes: • Collaborate with data engineering teams to improve data warehousing and ETL (Extract, Transform, Load) processes, ensuring data is readily available for analysis. By fulfilling these responsibilities, data analysts help organizations make informed decisions, optimize processes, and achieve their strategic objectives. Skills needed to be a data analyst 1. Programming Languages: • SQL: Essential for querying databases. • Python and R: Popular for data manipulation, statistical analysis, and visualization. 4. Data Manipulation and Cleaning: • Ability to preprocess, clean, and structure raw data into a usable format using libraries like Pandas (Python) or dplyr (R). 6. Statistical Analysis: • Proficiency in statistical methods and tests to interpret data and uncover insights. 8. Data Visualization: • Ability to present data findings visually using tools and libraries such as Matplotlib and Seaborn (Python), ggplot2 (R), Tableau, or Power BI. 10. Database Management: • Understanding of relational databases, database design, and the ability to use SQL to extract, manipulate, and analyze data. • Familiarity with NoSQL databases can also be beneficial. 13. Excel: • Advanced skills, including pivot tables, complex formulas, and various data analysis tools. 15. Big Data Tools: • Basic knowledge of big data platforms like Hadoop and Spark can be advantageous, especially for roles requiring analysis of large datasets. 17. Data Warehousing Solutions: • Familiarity with solutions like Amazon Redshift, Google BigQuery, or Snowflake for storing and analyzing large datasets. Data analysts play a vital role in helping businesses make informed decisions. By providing insights into customer behavior, market trends, and operational efficiency, data analysts can help businesses improve their bottom line. If you are interested in a career in data analysis, you should have a strong foundation in mathematics, statistics, and computer science. You should also be able to think critically and solve problems. Additionally, you should have strong communication and presentation skills. The field of data analysis is growing rapidly, and there is a high demand for qualified professionals. If you have the skills and experience necessary to be a data analyst, you can expect a rewarding career in a field that is making a real impact on the world. Data analyst vs Data scientist Data analyst vs Data scientist In general, data analysts are responsible for providing insights into existing data, while data scientists are responsible for developing new ways of generating insights from data. Data analysts typically have a strong foundation in statistics and data visualization, while data scientists typically have a strong foundation in machine learning and artificial intelligence. Both data analysts and data scientists play important roles in helping businesses make better decisions. However, the specific skills and responsibilities of each role differ. If you are interested in a career in data, it is important to understand the differences between these two roles so that you can choose the path that is best for you. Data Analyst Job Description Template [Company Name] is a [brief description of the company, e.g., "leading fintech company providing innovative solutions to global clients"]. We pride ourselves on [specific company attributes, e.g., "cutting-edge technology and data-driven decision-making"]. We're looking for a talented Data Analyst to join our growing team and help us turn raw data into valuable insights. Key Responsibilities: 1) Data Collection & Cleaning: Extract, preprocess, and clean data from diverse sources, ensuring its accuracy and reliability. 2) Analysis: Analyze data to identify patterns, trends, and anomalies, providing actionable insights to drive business strategies. 3) Reporting: Develop regular reports and dashboards for stakeholders using visualization tools like Tableau, Power BI, or custom solutions. 4) Collaboration: Work closely with various departments, including marketing, finance, and operations, to understand their data needs and provide support. 5) Statistical Models: Use statistical tools to interpret data sets and produce actionable recommendations. 6) Database Management: Query and manipulate databases using SQL or other querying languages. 7) Continuous Learning: Stay updated with the latest industry trends, tools, and best practices in data analysis. Required Skills & Qualifications: - Bachelor's degree in Mathematics, Economics, Computer Science, Information Management, Statistics, or a related field. - Proven experience as a Data Analyst or Business Data Analyst. - Technical expertise with data models, database design development, and segmentation techniques. - Strong proficiency in using statistical packages (e.g., R, Python) and data visualization tools (e.g., Tableau, Power BI). - Knowledge of SQL and relational databases. - Strong analytical skills with the ability to collect, organize, and analyze significant amounts of information with attention to detail and accuracy. - Excellent communication and collaboration skills Codersarts can help you achieve your data analyst goals. • Complete projects on time and to a high standard. • Gain the skills and experience you need to succeed in your career. • Develop your data analyst skills with our expert support. Contact us today to learn more about how we can help you.

The term "data scientist" was first coined in the early 2000s. It is a combination of the words "data" and "scientist". The term "scientist" is used to denote someone who is engaged in the systematic study of a particular subject. In the case of a data scientist, the subject is data. What Is a Data Scientist? Data scientists use a variety of methods to study data, including: • Statistics • Machine learning • Data mining • Data visualization They use these methods to extract insights from data that can be used to solve business problems. The term "data scientist" is often used interchangeably with the terms "data analyst" and "business intelligence analyst". However, there are some subtle differences between these terms. • Data analysts typically focus on collecting, cleaning, and analyzing data. They may also create reports and dashboards to communicate their findings to stakeholders. • Business intelligence analysts typically focus on using data to answer business questions. They may also develop and maintain data models. • Data scientists typically have a deeper understanding of statistical and machine learning methods. They may also be involved in developing new data analysis techniques. In general, the term "data scientist" is used to refer to someone who has a strong understanding of both data and the methods used to analyze it. Data scientists are in high demand, and the field is expected to grow much faster than average over the next decade. The term "data scientist" is a relatively new one, but it is already well-established. The term is likely to continue to be used as the field of data science grows and evolves. What are the skills needed to be a data scientist? The skills needed to be a data scientist include: Technical skills: • Programming: Data scientists need to be proficient in at least one programming language, such as Python, R, SAS, or SQL. • Statistics: Data scientists need to have a strong understanding of statistical concepts and methods. • Machine learning: Data scientists need to be familiar with machine learning algorithms and techniques. • Data wrangling: Data scientists need to be able to clean and prepare data for analysis. • Data visualization: Data scientists need to be able to create clear and concise visualizations of data. Proficiency with tools and libraries like Matplotlib, Seaborn (Python), and ggplot2 (R), Tableau, PowerBI and Excel • Big Data Technologies: Familiarity with platforms like: • Hadoop and Spark for processing large datasets. • Kafka for real-time data processing. Soft skills: • Communication: Data scientists need to be able to communicate their findings to both technical and non-technical audiences. • Problem-solving: Data scientists need to be able to identify and solve problems using data. • Critical thinking: Data scientists need to be able to think critically about data and identify patterns and trends. • Creativity: Data scientists need to be able to think creatively about how to use data to solve problems. • Business acumen: Data scientists need to have a basic understanding of business principles. In addition to these skills, data scientists also need to be able to stay up-to-date on the latest trends in data science. The field of data science is constantly evolving, so it is important for data scientists to be lifelong learners. If you are interested in a career in data science, you can start by developing the skills listed above. You can take courses, read books, and participate in online communities to learn more about data science. You can also gain experience by working on data science projects. Educational Background: While many data scientists have advanced degrees in fields like computer science, statistics, or operations research, it's not uncommon for professionals with bachelor's degrees and relevant experience to transition into data science roles. Continuous learning, both formal and informal, is integral to staying current in the ever-evolving field of data science. With the right skills and experience, you can be a successful data scientist.

In this articel, we're going to understand What does a prompt engineer do? Roles, Responsibilty and Skills. Before going to see details let's undestand What is Prompt. In this article, we will understand what a prompt engineer does, their roles, responsibilities, and skills. Before going into details, let's understand what a prompt is. What is Prompt? A prompt is an instruction or question that is given to a computer system in order to guide its behavior and perform an operation based on the user's input. In simpler terms, a prompt is a user's input given to a computer program to perform a certain operation. For example, the prompt "Enter your name" would instruct the computer to accept the user's name as input. The prompt "What is the capital of India?" would instruct the computer to search for the answer to the question and provide it to the user. Prompts can be used to guide the behavior of a wide variety of computer programs, including: • Operating systems • Programming languages • Databases • Web applications • Machine learning models By carefully crafting prompts, users can ensure that computer programs are able to perform tasks effectively and generate the desired outputs. Let's understand with a computer program. Example: print("Enter a name:") name = input() print("Hello, " + name + "!") This is Python code. When you run this program, you will get the message "Enter a name:" at the terminal. This is called a prompt message. When you enter a name as input, such as "Jitendra", this is called user input for the prompt message "Enter a name". The term "prompt engineer" is a relatively new one, first appearing in the early 2020s. Due to advent of new term Generative AI. Generative AI are machine learning model to get generate answer based on the user input or questions called Prompt. Generative AI models are capable of producing creative text formats, like poems, code, scripts, musical pieces, email, letters, etc., but they need guidance to do so. This guidance comes in the form of prompts. Prompt engineers are responsible for designing and developing prompts that can elicit high-quality, relevant, and unbiased outputs from generative AI models. They need to have a deep understanding of both the generative AI model and the task at hand in order to create effective prompts. Prompt engineering is a technique used in Generative AI to optimize and fine-tune a large language models(LLMs) for particular tasks and desired outputs. Also known as prompt design, it refers to the process of carefully constructing prompts or inputs for AI models to enhance their performance on specific tasks. A large language model (LLM) is an AI or machine learning model responsible for generating specific outputs based on user prompts. Essentially, it's a machine learning model. "A large language model (LLMs)" refers to a type of artificial intelligence model designed to understand and generate human-like text based on vast amounts of data it has been trained on. These models are "large" because they have billions or even trillions of parameters, which allow them to process and produce complex text. LLMs can answer questions, write essays, generate creative content, and more, based on the patterns they've learned from the data. For "a large language model (LLMs)," "fine-tune" means adjusting the model after its initial training by using a smaller, specific set of data. It's like having a dictionary that knows all languages but then giving it a special lesson on slang from a particular region. This helps the model become more specialized or better at understanding that specific type of language or content. In short, generative AI models are the tools, and prompt engineers are the artists. By working together, they can create stunning works of art. Roles and Responsibilities • Design, develop, and test prompts for a variety of machine learning models • Conduct research on new prompt engineering techniques • Evaluate the performance of prompts and make improvements as needed • Collaborate with machine learning engineers and other members of the team to develop and improve machine learning models • Stay up-to-date on the latest developments in prompt engineering and natural language processing (NLP) • Communicate effectively with both technical and non-technical audiences. Skills • Fundamental of Generative AI • Strong understanding of natural language processing (NLP) • Familiarity with machine learning (ML) concepts and techniques • Experience with prompt engineering or a related field • Strong writing and editing skills • Ability to think creatively and solve problems • Excellent communication and teamwork skills The field of prompt engineering is rapidly growing, and there is a high demand for qualified professionals. As machine learning models become more complex, the need for skilled prompt engineers will only increase. Prompt engineers have the opportunity to work on cutting-edge projects and make a real impact on the world.

Machine learning is a subset of artificial intelligence (AI) that emulates human intelligence by utilizing historical data in a similar manner to how students learn from books and are subsequently evaluated on their comprehension of the material through tests or exams. Machine Learning Machine learning algorithms enable software applications to enhance their predictive accuracy without the need for explicit programming. There are numerous machine learning algorithms available, each with its own strengths and weaknesses, depending on the desired output and the characteristics of the data. In essence, machine learning algorithms are trained on historical data, allowing them to identify patterns and relationships within the data. These patterns can then be used to make predictions about future events or outcomes. For example, a machine learning algorithm could be trained on historical sales data. The algorithm would then be able to identify patterns in sales data, such as seasonal trends or the impact of marketing campaigns. This information could then be used to make predictions about future sales. In simpler terms, machine learning is like teaching a computer to learn from experience. The more data a machine learning algorithm is given, the better it becomes at making predictions. What is a Machine Learning Engineer? Machine learning engineers build and train computer programs to learn from data and make predictions.  They do this by following a series of steps: 1. Data preprocessing: This involves cleaning and preparing the data so that it can be used by the machine learning model. 2. Feature engineering and selection: This involves identifying the most important features in the data for the machine learning model to learn from. 3. Model training: This involves teaching the machine learning model to learn from the data and make predictions. 4. Model testing and evaluation: This involves testing the machine learning model on new data to see how well it performs. 5. Model deployment: This involves making the machine learning model available to users so that they can use it to make predictions. Machine learning engineers also work on improving the performance of machine learning models and making them more efficient. They also work on developing new machine learning algorithms and techniques. Here is an even simpler explanation: Machine learning engineers teach computers to learn from data. They do this by building and training machine learning models. Machine learning models can be used to make predictions about new data, such as whether a customer is likely to churn or whether a medical image shows a tumor. Machine learning engineers are in high demand because machine learning is used in a wide variety of industries, including healthcare, finance, and technology. Responsibilities of a Machine Learning Engineer The responsibilities of a machine learning engineer (MLE) can vary depending on the size and structure of the organization they work for. However, some common responsibilities include: • Collecting, cleaning, and pre-processing data: MLEs are responsible for gathering data from a variety of sources, such as databases, sensors, and APIs. They then clean and pre-process the data to ensure that it is in a format that can be used by machine learning algorithms.  • Feature engineering: MLEs often need to create new features from existing data. This process is known as feature engineering. Feature engineering can help to improve the performance of machine learning models.  • Training, evaluating, and tuning machine learning models: MLEs train machine learning models on historical data. They then evaluate the performance of the models and make adjustments as needed. This process is known as model tuning.  • Deploying machine learning models to production: Once a machine learning model is trained and evaluated, it can be deployed to production. This means that the model can be used to make predictions on new data.  • Monitoring and maintaining machine learning models: MLEs are responsible for monitoring the performance of machine learning models in production. They also need to maintain the models by updating them with new data.  • Conducting research on new machine learning techniques: MLEs stay up-to-date on the latest developments in machine learning. They may also conduct research on new machine learning techniques.  In addition to these technical responsibilities, MLEs also need to have strong communication and teamwork skills. They need to be able to communicate effectively with stakeholders about machine learning projects. They also need to be able to work effectively with other engineers, data scientists, and product managers. MLEs play a critical role in the development and deployment of machine learning solutions. They are responsible for ensuring that machine learning models are accurate, reliable, and scalable. Skills Required to be a Machine Learning Engineer 1. Programming skills in languages: Python, R, and Java 2. Mathematics & Statistics: A strong foundation in: - Linear algebra: Helps in understanding vectors, matrices, eigenvalues, and eigenvectors, which are frequently used in ML algorithms. - Calculus: For understanding concepts like gradient descent. - Probability and statistics: To interpret data, models, and predictions. 3. Data Processing: Knowledge of: - Data preprocessing: Techniques like normalization and standardization. - Data wrangling tools: Such as Pandas and Numpy for data manipulation. 4. Machine Learning Algorithms: Familiarity with algorithms such as: - Supervised learning algorithms (e.g., linear regression, decision trees, support vector machines) - Unsupervised learning algorithms (e.g., clustering, dimensionality reduction techniques) - Ensemble methods (e.g., Random Forest, Gradient Boosting) 5. Deep Learning: Proficiency in neural networks and frameworks: - Frameworks: TensorFlow, Keras, PyTorch, Caffe, etc. - Neural Network Architectures: CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short Term Memory networks), and transformers. 6. Big Data Technologies: Familiarity with tools and platforms like: - Hadoop, Spark for processing large datasets. - Kafka for real-time data processing. 7. Cloud Platforms: Experience with: - AWS: Especially services like Sagemaker. - Google Cloud ML Engine, Azure Machine Learning, and other cloud-based ML platforms. 8. Databases: Knowledge of: - SQL databases (e.g., PostgreSQL, MySQL) and - NoSQL databases (e.g., MongoDB, Cassandra) for handling structured and unstructured data. 9. Software Engineering & System Design: - Ability to design robust, scalable, and production-ready systems. - Knowledge of version control systems like Git. 10. Evaluation Metrics: Ability to gauge the effectiveness of ML models using metrics like accuracy, precision, recall, F1 score, ROC, and AUC. 11. Domain Knowledge: - Depending on the specific application of ML (e.g., finance, healthcare, or e-commerce), domain-specific knowledge can be highly beneficial. 1. Soft Skills: - Problem-solving: To devise efficient solutions and algorithms. - Communication: To explain complex models and findings to non-experts. - Teamwork: Collaborating with data scientists, software engineers, and business analysts. 2. Continuous Learning: - The field of machine learning evolves rapidly. Staying updated with the latest research, tools, and techniques is crucial. This list covers the foundational skills. However, the field of machine learning is vast, and specializations can require additional expertise. It's essential for aspiring Machine Learning Engineers to continuously learn and adapt in this dynamic field. How to Become a Machine Learning Engineer There are a number of ways to become a machine learning engineer. Some common paths include: • Completing a degree in computer science, data science, or a related field • Taking online courses or tutorials on machine learning • Gaining hands-on experience with machine learning through projects and competitions • Contributing to open source machine learning projects What are the salary expectations for machine learning engineers? The salary expectations for machine learning engineers (MLEs) vary depending on a number of factors, such as experience, location, and industry. However, in general, MLEs can expect to earn competitive salaries. According to a recent survey by Indeed, the average salary for a machine learning engineer in the United States is $153,662 per year. However, salaries can range from as low as $100,000 per year to as high as $250,000 per year or more. MLEs with more experience can expect to earn higher salaries. For example, MLEs with 5-7 years of experience can expect to earn an average salary of $178,608 per year. MLEs with 10+ years of experience can expect to earn an average salary of $221,573 per year. Location can also impact salary expectations. For example, MLEs in San Francisco can expect to earn an average salary of $197,997 per year. MLEs in New York City can expect to earn an average salary of $186,364 per year. MLEs in Seattle can expect to earn an average salary of $172,707 per year. Finally, industry can also impact salary expectations. For example, MLEs in the technology industry can expect to earn an average salary of $168,995 per year. MLEs in the finance industry can expect to earn an average salary of $162,321 per year. MLEs in the healthcare industry can expect to earn an average salary of $157,234 per year. Overall, the salary expectations for machine learning engineers are high. MLEs with the right skills and experience can expect to earn competitive salaries.

An AI engineer is a professional who designs, develops, and deploys artificial intelligence (AI) systems. They have a deep understanding of machine learning algorithms, natural language processing, computer vision, and other AI techniques. AI engineers are responsible for the entire lifecycle of an AI system, from conception to deployment. They work closely with data scientists, product managers, and other stakeholders to gather requirements, design and develop AI models, and integrate them into existing systems. Responsibilities of an AI Engineer • Gathering and analyzing data • Developing and implementing machine learning models • Testing and evaluating AI models • Deploying and maintaining AI models • Conducting research on new AI techniques • Communicating with stakeholders about AI projects Skills required to be an AI Engineer • Strong technical skills in computer science, mathematics, and machine learning • Programming skills in languages such as Python, Java, or R • Data analysis and visualization skills • Problem-solving and critical thinking skills • Communication and teamwork skills Projects to include in your portfolio As an AI engineer, you should include projects in your portfolio that demonstrate your skills in the following areas: • Machine learning model development • Data preprocessing and analysis • Model evaluation and testing • AI system deployment How to become an AI Engineer • Earn a degree in computer science, data science, or a related field • Take courses in machine learning, natural language processing, and computer vision • Gain hands-on experience with machine learning libraries and frameworks • Participate in hackathons and competitions • Contribute to open source AI projects • Network with other AI professionals Career prospects for AI Engineers The demand for AI engineers is high and is expected to grow in the coming years. AI engineers are employed in a variety of industries, including technology, finance, healthcare, and retail. Additional tips for aspiring AI Engineers • Gain hands-on experience with machine learning libraries and frameworks. • Participate in hackathons and competitions to showcase your skills. • Contribute to open source AI projects. • Network with other AI professionals. • Stay up-to-date on the latest trends in AI development. By following these tips, you can increase your chances of success in the field of AI engineering. Salary of an AI Engineer The median salary for an AI engineer in the United States is $130,690 per year. AI engineering is a challenging and rewarding career. If you are interested in working on cutting-edge technologies and have the skills and knowledge required to be successful in this field, then a career in AI engineering may be right for you.

Job Description: As a Backend AI Developer, you will be responsible for designing, developing, and maintaining the backend infrastructure for artificial intelligence (AI) applications. You will work closely with data scientists, machine learning engineers, and frontend developers to create scalable and performant AI systems. AI Development Responsibilities: • Design and develop backend APIs for AI applications • Implement and maintain machine learning models in a production environment • Optimize backend systems for performance and scalability • Integrate AI models with existing backend systems • Develop and maintain data pipelines for AI applications • Troubleshoot and debug backend AI issues • Stay up-to-date on the latest advances in AI development Qualifications: • Strong understanding of backend development principles and practices • Experience with at least one programming language (e.g., Python, Java, Go) • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) • Experience with cloud computing platforms (e.g., AWS, Azure, GCP) • Strong problem-solving and debugging skills • Excellent communication and teamwork skills Benefits: • The opportunity to work on cutting-edge AI projects • The chance to make a real impact on the world • The opportunity to learn and grow in a rapidly evolving field • A competitive salary and benefits package • The chance to work with a team of talented and passionate engineers Image of Backend AI Developer In the realm of artificial intelligence (AI), the backend AI developer plays a pivotal role in bringing AI-powered applications to life. While their counterparts, frontend AI developers, focus on the user interface and user experience, backend AI developers delve into the intricate depths of the underlying infrastructure that supports AI models and algorithms. What does a Backend AI Developer do? Backend AI developers are responsible for designing, developing, and maintaining the backend systems that power AI applications. This includes: • Designing and developing APIs for AI models • Implementing and maintaining machine learning models in production • Optimizing backend systems for performance and scalability • Integrating AI models with existing backend systems • Developing and maintaining data pipelines for AI applications • Troubleshooting and debugging backend AI issues How to prepare for a career as a Backend AI Developer To become a successful backend AI developer, you should have a strong foundation in computer science fundamentals, such as: • Data structures and algorithms • Object-oriented programming • Databases • Operating systems • Networking In addition to these fundamentals, you should also have a strong understanding of machine learning concepts and techniques. This includes: • Supervised learning • Unsupervised learning • Reinforcement learning • Deep learning Essential skills for Backend AI Developers In addition to technical skills, backend AI developers should also possess the following soft skills: • Problem-solving skills: Backend AI developers are often tasked with solving complex problems related to the design and implementation of AI systems. • Analytical skills: Backend AI developers must be able to analyze large amounts of data to identify patterns and trends. • Communication skills: Backend AI developers must be able to effectively communicate with other developers, data scientists, and stakeholders. • Teamwork skills: Backend AI developers often work as part of a team to develop and maintain AI systems. Projects to include in your portfolio As a backend AI developer, you should include projects in your portfolio that demonstrate your skills in the following areas: • API development • Machine learning model implementation • Backend system optimization • Data pipeline development • Troubleshooting and debugging Conclusion A career as a backend AI developer is both challenging and rewarding. If you are interested in a career in AI, and you have the necessary skills and knowledge, then backend AI development may be the right path for you. Additional tips for aspiring Backend AI Developers • Stay up-to-date on the latest trends in AI development. • Contribute to open source AI projects. • Network with other AI professionals. • Attend AI conferences and meetups. • Build a strong portfolio of AI projects. By following these tips, you can increase your chances of success in the field of backend AI development.

In this Article, we undertake a rigorous examination of AI-powered subtitles and shorts. We delve into the intricate details of their technical implementation, shedding light on the sophisticated algorithms and architectures that underpin their functionality. Furthermore, we explore a wide range of captivating use cases, demonstrating the immense potential of this technology to enhance accessibility, improve comprehension, and boost engagement. Additionally, we delve into the significance of AI-powered subtitles and shorts, highlighting their impact on the content creation and consumption landscape. By delving into these key aspects, we provide a comprehensive overview of this groundbreaking innovation, offering valuable insights to those seeking to understand its potential and implications. Use cases of an application for automatic subtitles and shorts with AI: • Content creation: The application can be used to create subtitles for videos, podcasts, and other forms of audio content. This can be helpful for creators who want to make their content more accessible to a wider audience, such as people who are deaf or hard of hearing, or people who are learning a new language. • Education and training: The application can be used to create educational videos with subtitles. This can be helpful for students who are learning a new subject, or for employees who are undergoing training. • Live captioning: The application can be used to provide real-time transcription of events, such as lectures, conferences, and meetings. This can be helpful for people who are deaf or hard of hearing, or for people who want to follow along with a presentation. • Social media marketing: The application can be used to create short videos with subtitles for social media platforms. This can be helpful for businesses and individuals who want to create engaging and informative content for their followers. • Video accessibility: The application can be used to make videos more accessible to people with disabilities. For example, the application can be used to create subtitles for videos that are not originally captioned, or to create audio descriptions for videos that are not originally described. Importance of an application for automatic subtitles and shorts with AI: • Increased accessibility: Automatic subtitles and shorts can make content more accessible to a wider audience, including people who are deaf or hard of hearing, people who are learning a new language, and people who are in noisy environments. • Improved comprehension: Subtitles can help people to better understand the content of a video, especially if the audio is unclear or if the speaker has a strong accent. • Engaged audience: Short videos with subtitles are more likely to be watched and shared than videos without subtitles. This is because subtitles can help people to quickly understand the content of a video, even if they are not able to watch the entire video. • SEO benefits: Subtitles can help videos to rank higher in search engine results pages (SERPs). This is because search engines can index the text in subtitles. • Time savings: Automatic subtitles and shorts can save time for content creators. This is because creators do not have to manually create subtitles or shorts. In addition to the above, automatic subtitles and shorts can also be used to create new forms of content, such as video summaries and video transcripts. These forms of content can be used to promote videos, to provide additional information about videos, or to create educational resources. Implementation Details: Frontend: • ReactJS: A JavaScript library for building user interfaces. ReactJS is known for its declarative, efficient, and flexible nature. • Next.js: A React framework that provides features such as server-side rendering, static site generation, and incremental static regeneration. • Tailwind CSS: A utility-first CSS framework that provides low-level building blocks for rapidly developing custom user interfaces. Backend: • Python: A general-purpose programming language that is widely used in the field of machine learning. Python is known for its ease of use and readability. • Django REST framework: A Django extension that provides a powerful and flexible toolkit for building REST APIs. • PostgreSQL: A powerful, open-source relational database that is known for its reliability and scalability. AI Model: • DeepSpeech: An open-source speech recognition model that is known for its accuracy and speed. • Hugging Face Transformers: A library of state-of-the-art natural language processing models. Transformers are particularly well-suited for tasks such as text summarization. Features: • Automatic subtitle generation for videos: The application will use DeepSpeech to transcribe the audio in videos and then use Hugging Face Transformers to generate subtitles. • Short video creation from long videos: The application will allow users to create short videos from long videos by selecting a specific segment of the video. • Real-time transcription: The application will provide real-time transcription of audio, which can be useful for tasks such as live captioning. • Speaker identification: The application will be able to identify different speakers in a video and generate subtitles for each speaker. • Translation into multiple languages: The application will be able to translate subtitles into multiple languages. • Customization of subtitles (font, size, color, etc.): The application will allow users to customize the appearance of subtitles. • Editing of subtitles: The application will allow users to edit subtitles before they are generated. Architecture: • Microservices architecture: A microservices architecture is a style of software development in which complex applications are composed of small, independent services. This style of architecture is known for its flexibility, scalability, and resilience. • REST API: A REST API is a type of web API that uses HTTP requests to represent resources. REST APIs are known for their simplicity and ease of use. • Containerized application: A containerized application is an application that is packaged into a container image. Container images are lightweight and portable, and they can be easily deployed to different environments. • Managed service: A managed service is a cloud computing service that is managed by the cloud provider. Managed services can be used to offload the burden of managing infrastructure. Deployment: • Cloud platform: A cloud platform is a type of computing platform that provides a set of services that can be used to build and deploy applications. • Static website: A static website is a website that is composed of pre-rendered HTML, CSS, and JavaScript files. Static websites are known for their performance and security. • Containerized application: A containerized application is an application that is packaged into a container image. Container images are lightweight and portable, and they can be easily deployed to different environments. • Managed service: A managed service is a cloud computing service that is managed by the cloud provider. Managed services can be used to offload the burden of managing infrastructure. Timeline: • Development: 3 months • Testing: 1 month • Deployment: 1 month Benefits: • High accuracy of subtitles: The use of DeepSpeech and Hugging Face Transformers will ensure that the application generates high-quality subtitles. • Fast turnaround time: The application will be able to generate subtitles quickly, which can be helpful for tasks such as live captioning. • Cost-effective: The use of open-source software will help to keep the cost of the application down. • Scalable: The application will be able to handle a large number of users and videos. • Customizable: The application will allow users to customize the appearance of subtitles. Conclusion: This application will provide a comprehensive solution for automatic subtitle generation and short video creation. The use of AI will ensure high accuracy and a fast turnaround time. The application will be cost-effective, scalable, and customizable. In addition to the above, the application could also include the following features: • Audio enhancement: The application could use audio enhancement techniques to improve the quality of the audio before it is transcribed. This could help to improve the accuracy References: • SendShort.ai(http://SendShort.ai) • Submagic.co(http://Submagic.co) • Captions.ai(http://Captions.ai) Codersarts AI: Proposal for Automatic Subtitles and Shorts Application Dear Start up owner / entrepreneurs , We are writing to express our keen interest in your job posting for an expert to develop a desktop and mobile application with automatic subtitles and long video shorts. We at Codersarts AI are a team of experienced and passionate AI developers who are dedicated to helping businesses achieve their goals through the power of artificial intelligence. We have a proven track record of success in developing high-quality, scalable, and user-friendly AI solutions. We have carefully reviewed your requirements and are confident that we have the skills and expertise to deliver a solution that meets your needs. We have a deep understanding of the state-of-the-art in automatic speech recognition (ASR), natural language processing (NLP), and machine learning (ML) techniques. We are also familiar with the applications you have referenced and are confident that we can develop a solution that is equal or better. Our proposed solution will include the following features: • Automatic subtitles generation: Our application will use state-of-the-art ASR models to generate accurate and high-quality subtitles for videos. • Long video shorts creation: Our application will allow users to create short videos from long videos by selecting a specific segment of the video. • Customization of subtitles: Users will be able to customize the appearance of subtitles, such as font, size, color, and position. • Editing of subtitles: Users will be able to edit subtitles before they are generated. • Translation of subtitles: Our application will be able to translate subtitles into multiple languages. We are committed to providing our clients with the highest quality of service. We will work closely with you throughout the development process to ensure that your needs are met and that you are satisfied with the final product. We are confident that we can deliver a solution that will help you achieve your mission of helping small entrepreneurs improve the presentation of their videos. We would be happy to provide you with a more detailed proposal upon request. Thank you for your time and consideration. We look forward to hearing from you soon. Sincerely, The Codersarts AI team

A powerful search engine that can index and search across a variety of content types, including documents, images, videos, and chat conversations. The search engine would use deep learning models to understand the context of content, enabling users to find relevant results even when their queries are not exact matches. Features: • Support for a wide range of content types, including documents, images, videos, and chat conversations • Deep learning models for understanding the context of content • Ability to find relevant results even when queries are not exact matches • Robust and comprehensive categorization system • Automatic updates to index and search as new content is added • User-configurable priority settings for cloud and local folders Benefits: • Improved search accuracy and relevance • Increased productivity through faster and more efficient search • Enhanced user experience through a more intuitive and natural search interface • Reduced costs associated with manual content tagging and classification Technical feasibility: Codersarts AI has extensive experience in developing deep learning models for natural language processing and computer vision. We are confident that we can build a search engine that meets the client's requirements. We would be happy to discuss this project with you in more detail. Please contact us to arrange a meeting.

We are excited to launch this forum as a space for our community of AI enthusiasts, developers, and researchers to come together to share ideas, ask questions, and collaborate on projects. We hope that this forum will be a valuable resource for anyone interested in AI, regardless of their level of experience. We encourage you to participate in discussions, ask questions, and share your own knowledge and expertise. We are also committed to maintaining a respectful and inclusive environment for all members of our community. We ask that you please be mindful of your language and behavior, and that you avoid making any discriminatory or offensive remarks. We hope that you enjoy this forum and that it becomes a valuable resource for you. Thank you for joining us! The Codersarts Team

Codersarts AI is a platform that provides a suite of tools and services to help businesses and individuals build, deploy, and manage artificial intelligence (AI) applications. Codersarts AI offers a variety of services, including: • Custom AI development: Codersarts AI can help businesses build custom AI applications that are tailored to their specific needs. • AI consulting: Codersarts AI can provide businesses with expert advice on how to use AI to solve their business problems. • AI training: Codersarts AI offers a variety of training courses on AI topics, such as machine learning, natural language processing, and computer vision. • AI infrastructure: Codersarts AI provides businesses with the infrastructure they need to build and deploy AI applications, such as cloud computing and data storage. Codersarts AI is committed to making AI accessible to everyone. The company offers a variety of resources to help people learn about AI, such as blog posts, tutorials, and webinars. Codersarts AI can help a wide range of businesses and individuals, including: • Startups: Codersarts AI can help startups build and deploy AI applications that can give them a competitive advantage. • Small and medium-sized businesses (SMBs): Codersarts AI can help SMBs use AI to automate tasks, improve efficiency, and gain insights from their data. • Enterprises: Codersarts AI can help enterprises build and deploy AI applications that can improve customer experience, optimize operations, and reduce costs. • Individuals: Codersarts AI can help individuals learn about AI and develop the skills they need to build their own AI applications. In addition to these specific groups, Codersarts AI can also help anyone who is interested in using AI to solve problems. The company's suite of tools and services can be used to build a wide range of AI applications, and the company's training and consulting services can help people get the most out of their AI investments. Codersarts AI offers a variety of services that can be helpful for students, including: • AI tutoring: Codersarts AI can provide students with one-on-one tutoring on AI topics. This can be a great way to get help with specific concepts or to learn about new AI techniques.  • AI projects: Codersarts AI can help students complete AI projects. This can be a great way to gain hands-on experience with AI and to learn how to apply AI to real-world problems.  • AI research: Codersarts AI can help students conduct AI research. This can be a great way to learn about the latest advances in AI and to contribute to the field of AI.  • AI competitions: Codersarts AI can help students participate in AI competitions. This can be a great way to test their skills against other students and to gain recognition for their work in AI.  • AI internships: Codersarts AI can help students find AI internships. This can be a great way to gain experience working on real-world AI projects and to learn from experienced AI professionals.  In addition to these services, Codersarts AI also offers a variety of resources that can be helpful for students, such as: • Blog posts: Codersarts AI publishes a blog that covers a wide range of AI topics. This can be a great way to learn about the latest trends in AI and to get insights from experts in the field.  • Tutorials: Codersarts AI offers a variety of tutorials on AI topics. This can be a great way to learn how to use specific AI tools and techniques.  • Webinars: Codersarts AI hosts webinars on a variety of AI topics. This can be a great way to learn from experts in the field and to ask questions about AI.  • Community forum: Codersarts AI maintains a community forum where students can ask questions and get help from other students and from experts in the field.  Codersarts AI is a valuable resource for students who are interested in learning about AI and who are looking for ways to gain experience in the field. The company's services and resources can help students learn about AI, develop their AI skills, and gain experience working on real-world AI projects. Codersarts AI offers a variety of services that can be helpful for developers, including: • Custom AI development: Codersarts AI can help developers build custom AI applications that are tailored to their specific needs.  • AI consulting: Codersarts AI can provide developers with expert advice on how to use AI to solve their development problems.  • AI training: Codersarts AI offers a variety of training courses on AI topics, such as machine learning, natural language processing, and computer vision.  • AI infrastructure: Codersarts AI provides developers with the infrastructure they need to build and deploy AI applications, such as cloud computing and data storage.  • AI APIs: Codersarts AI offers a variety of APIs that developers can use to add AI functionality to their applications.  In addition to these services, Codersarts AI also offers a variety of resources that can be helpful for developers, such as: • Blog posts: Codersarts AI publishes a blog that covers a wide range of AI topics. This can be a great way to learn about the latest trends in AI and to get insights from experts in the field.  • Tutorials: Codersarts AI offers a variety of tutorials on AI topics. This can be a great way to learn how to use specific AI tools and techniques.  • Webinars: Codersarts AI hosts webinars on a variety of AI topics. This can be a great way to learn from experts in the field and to ask questions about AI.  • Community forum: Codersarts AI maintains a community forum where developers can ask questions and get help from other developers and from experts in the field.  Codersarts AI is a valuable resource for developers who are interested in using AI to build better software. The company's services and resources can help developers learn about AI, develop their AI skills, and build AI-powered applications. Codersarts AI is a valuable resource for businesses and individuals who are looking to use AI to solve their problems. The company's suite of tools and services can help businesses build, deploy, and manage AI applications, and the company's training and consulting services can help businesses get the most out of their AI investments.

Remember, the goal of the Codersarts AI Forum is to foster a positive and educational community where members can learn, share, and grow. Let's work together to make it a valuable resource for everyone! Codersarts AI Forum Rules 1. Be respectful and civil. We expect all forum members to treat each other with respect, even when they disagree. Personal attacks, insults, and other forms of abusive behavior will not be tolerated. 2. Stay on topic. The Codersarts AI Forum is a place to discuss AI-related topics. Off-topic posts will be removed. 3. Do not spam or advertise. The Codersarts AI Forum is not a place to promote your products or services. Spam and advertising will be removed. 4. Do not post copyrighted material. If you post copyrighted material, you must have the permission of the copyright holder. 5. Do not post illegal or harmful content. The Codersarts AI Forum is not a place to post illegal or harmful content. This includes, but is not limited to, child pornography, hate speech, and threats of violence. Violations of these rules may result in: • Your post being removed • Your account being suspended • Your account being banned We reserve the right to take any action we deem necessary to maintain a respectful and productive forum environment. Thank you for your cooperation.