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Google Machine Learning & AI

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What is a Google Machine Learning?

In this path, you’ll explore Google Cloud products like BigQuery, Datalab, and TensorFlow and how to integrate with machine learning APIs such as Cloud Vision or Natural Language API.

Process Flow, How you can proceed it?

  • Big Data & Machine Learning Fundamentals

  • Perform Foundational Data, ML, and AI Tasks in Google Cloud

  • Machine Learning on Google Cloud

  • Automate Interactions with Contact Center AI

  • Advanced Machine Learning with TensorFlow on Google Cloud Platform

  • Explore ML models with Explainable AI

  • MLOps (Machine Learning Operations) Fundamentals

End-to-end machine learning life cycle

  • Prepare: Prepare and store your datasets with BigQuery and Cloud Storage, then use the built-in Data Labeling Service to label your training data for classification, object detection, entity extraction, and other objectives for image, video, tabular, and text data.  

  • Build:  Build best-in-class ML models without writing any code with AutoML's easy-to-use UI, or using your own code written in Notebooks, a managed Jupyter Notebook service.

  • Validate: Validate your model with AI Explanations and What-If Tool, which help you understand your model's outputs, verify model behavior, identify bias, and find ways to improve your model and training data.

  • Deploy: Deploy your models at scale to get predictions in the cloud with Prediction, which hosts your model for online and batch prediction requests.

  • MLOps: Manage your models, experiments, and end-to-end workflows with Pipelines by applying MLOps best practices with robust, repeatable pipelines

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