How do you integrate Machine Learning with Data Lakes?

Data lakes are ideal for ML workloads, providing access to vast, diverse datasets. Integration includes feature engineering pipelines transforming raw data into ML features, feature stores centralizing feature definitions and serving, model training using data lake datasets, MLOps pipelines automating training/deployment, and model scoring on data lake data. Technologies: Databricks MLflow, AWS SageMaker, Azure ML, Kubeflow. Best practices: version datasets for reproducibility, track lineage from raw data to features, automate feature freshness, monitor model drift, use Delta for ACID guarantees.

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