ML Pipeline Patterns

What problem does it solve?

This Skill provides expert patterns and best practices for constructing reliable and efficient batch and streaming Machine Learning data pipelines.

Core Features & Use Cases

• Batch & Streaming: Covers patterns for both types of data processing.
• Frameworks: Integrates with Apache Beam, Spark, Flink, and dbt.
• Quality & Validation: Includes patterns for data quality checks with Great Expectations.
• Use Case: You need to build a real-time feature pipeline that aggregates user activity from Kafka, computes rolling window features, and stores them in a feature store. This Skill offers patterns for event processing, windowing, and sink integration.

Quick Start

Use the ML Pipeline Patterns skill to build a batch data pipeline using Apache Beam that normalizes features from a Parquet file.