Using MLOps to Build a Real-time End-to-End Machine Learning Pipeline | Binance Blog

The computing layer is mainly responsible for feature engineering, the process of transforming raw data into useful features.

The computing layer's input data generally comes from the event-based database, which includes Apache Kafka and Kinesis, or the OLAP database, which includes Apache Hive for open-source and Snowflake for cloud solutions.The store layer is where we register feature definitions and deploy them into our feature store as well as perform backfill, a process that allows us to rebuild features via historical data whenever a new feature is defined.

Decoupling computing and ingestion provides a variety of benefits for our ML pipelines. For starters, we can increase the pipeline's robustness in case of failures. Our data scientists can still pull feature value from the centralized database, even if the ingestion or computing layer is unavailable due to operational, hardware, or network issues.

Both the computing layer and the store layer are what we call automated feature pipelines. These pipelines are independent, run on varying schedules, and are categorized as streaming or batch pipelines. Here's how the two pipelines work differently: one feature group in a batch pipeline might refresh nightly while another group is updated hourly. In a streaming pipeline, the feature group updates in real-time as source data arrives on an input stream, such as an Apache Kafka topic.

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