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Databricks Series, Part 6: ML Serving and Workflows

Batch and real-time model inference, Databricks Model Serving endpoints, and orchestrating the full ML pipeline with Databricks Workflows.

Databricks Series, Part 5: Machine Learning with MLflow

Tracking experiments, logging models and artifacts, comparing runs, and managing the model lifecycle with MLflow on Databricks.

Databricks Series, Part 4: Feature Engineering at Scale

Databricks Feature Store, FeatureEngineeringClient, FeatureLookup, training sets, and eliminating training-serving skew.

Databricks Series, Part 3: Data Ingestion with Auto Loader

cloudFiles format, schema inference, schema evolution, and building robust incremental ingestion pipelines on Databricks.

Databricks Series, Part 2: Lakehouse Architecture

Unity Catalog for governance and discovery, the medallion Bronze/Silver/Gold pattern, and Delta tables as the storage foundation.

Databricks Series, Part 1: Getting Started

Navigating the Databricks workspace, launching clusters, writing notebooks, and submitting your first PySpark job.

Databricks Series, Part 0: Overview

The lakehouse platform concept, what Databricks adds on top of Spark and Delta Lake, and how it compares to alternatives.