ML Pipeline Patterns

Build streaming ML data pipelines using patterns for event processing, windowing, and sink integration. These patterns process real-time user activity from sources like Kafka, compute rolling window features, and store them in a feature store.

Validate data quality in ML pipelines by integrating Great Expectations validation patterns. This approach ensures robust MLOps workflows by checking data preprocessing outputs and feature generation logic against expected constraints.

Use dbt for incremental modeling and feature preprocessing within batch ML pipelines. The provided patterns facilitate robust data transformation, normalization, and ETL processing to prepare features for machine learning models.

Yes, the patterns support both batch and streaming ML data pipelines. You can build batch processing pipelines with Spark and real-time computation pipelines with Flink, ensuring flexible MLOps data preparation workflows.

Normalize features from a Parquet file by applying batch data pipeline patterns with Apache Beam. This process executes ETL and data preprocessing steps to transform raw data into structured, model-ready feature sets.

Choose Flink or Apache Beam for real-time computation based on your specific streaming architecture needs. The patterns address event processing and windowing for both frameworks, enabling robust feature generation tailored to your MLOps environment.