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When we think of modern data processing, we often think of batch-oriented ecosystems like Hadoop, including processing engines like Spark. However, the sooner we can extract useful information from our data, the better, which is driving an evolution towards stream processing or “fast data”. Many of the legacy tools, including Spark, provide various levels of support for stream processing, but deeper architectural changes are emerging.
Then we’ll work through code examples that use Akka Streams and Kafka Streams with Kafka to implement a machine-learning example where a machine learning model is updated periodically to simulate the problem of periodic retraining and serving of ML models in a streaming context. In particular, if you periodically retrain the model using one tool chain, for example, once a day, how to do you incorporate the updated model into a running pipeline for scoring without restarting the pipeline?
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