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Kafka In Machine Learning
What is a Kafka in Machine Learning?
Machine Learning and the Apache Kafka Ecosystem are an excellent combination for training and deploying scalable analytical models, the deployment of an analytic model in a Kafka application for real-time predictions.
Model deployment in Kafka applications
Kafka applications are event-based and use event streams to continuously process incoming data. If you use Kafka, you can natively embed an analytical model in a Kafka Streams or KSQL application. There are several examples of Kafka Streams microservices that incorporate models natively created with TensorFlow, H2O, or Deeplearning4j.
Reasons for Apache Kafka’s success
One of the reasons for Apache Kafka’s success is its rapid adoption and acceptance by many technology companies. Almost all major Silicon Valley companies such as LinkedIn, Netflix, Uber or eBay blog.
Kafka is a logical addition to an ML platform: Training, monitoring, deployment, inferencing, configuration, A/B testing, etc.
Why Industries Use Kafka's?
Apache Kafka can be viewed as the key to a flexible and sustainable infrastructure for modern machine learning. The ecosystem surrounding Apache Kafka is the perfect complement to an ML architecture. Choose the components you need to build a scalable, reliable platform that is independent of any specific on-premise/cloud infrastructure or ML technology.
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