How can Delta Live Tables (DLT) be used in an ML pipeline context?
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Short Explanation and Infographic
DLT is the data prep powerhouse upstream of your ML pipeline — it handles incremental ingestion, transformation, and quality checks so your feature engineering is always fresh and reliable when training kicks off.
Full explanation below image
Full Explanation
Delta Live Tables (DLT) is a declarative ETL framework for building reliable data pipelines. In ML context, DLT manages the data preparation tier: @dlt.table decorators define materialized views (Bronze → Silver → Gold) for ingesting, cleaning, and transforming raw data into ML-ready features. DLT provides: Data quality expectations (@dlt.expect() annotations enforce data quality rules — quarantine or fail on bad data). Automatic dependency management (DLT infers the pipeline DAG). Incremental processing (only processes new data). Monitoring and lineage (tracks data flow from source to feature table). Typical ML pipeline with DLT: DLT pipeline outputs feature data to Delta table → Databricks Job triggers ML training → model registered to MLflow → deployed to serving. DLT does NOT train models (it's ETL, not ML), doesn't replace MLflow Registry, and doesn't auto-deploy to serving.