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Be sure to check out his talk, “ ApacheKafka for Real-Time Machine Learning Without a DataLake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the ApacheKafka ecosystem.
It initially sources input time series data from Amazon Managed Streaming for ApacheKafka (Amazon MSK) using this live stream for model training. Post-training, the model continues to process incoming data points from the stream. It evaluates these points against the historical trends of the corresponding time series.
And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, datalakes became data swamps. Some datalake implementations required expensive ‘cleansing pumps’ to make them navigable again. Subscribe to Alation's Blog.
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Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a datalake.
For every xSaves prediction, it produces a message with the prediction as a payload, which then gets distributed by a central message broker running on Amazon Managed Streaming for ApacheKafka (Amazon MSK). The information also gets stored in a datalake for future auditing and model improvements.
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