Enterprise AI and Data Engineering with Databricks: Coursera Specialization

2026-04-17 · 4min · Pragmatic AI Labs

Table of Contents

Master the Databricks lakehouse end-to-end: Delta Lake data engineering, MLflow experiment tracking, generative AI on the platform, and the production governance patterns that keep regulated ML workloads compliant.

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What You Will Build

A full lakehouse pipeline — bronze/silver/gold Delta tables, MLflow-tracked model training, an LLM-backed GenAI application, and the Unity Catalog / cluster-governance scaffolding to run it in an enterprise environment.

Courses in This Specialization

Databricks Lakehouse Fundamentals — Architecture, workspaces, clusters, notebooks, SQL Warehouses, and how the lakehouse differs from warehouse and lake.
Data Engineering with Delta Lake on Databricks — Delta tables, ACID transactions, streaming ingest, Unity Catalog, and medallion architecture.
Machine Learning with Databricks and MLflow — MLflow tracking, model registry, AutoML, feature store, and serverless model serving.
Generative AI and LLMs on Databricks — Foundation Model APIs, vector search, RAG on Databricks, and Mosaic AI.
Production Governance and MLOps on Databricks — Lakehouse Monitoring, model governance, audit, and cost observability for regulated workloads.

Who This Is For

Data engineers on the lakehouse adoption path
ML engineers who need MLflow-native workflows
Platform and governance teams running Databricks at scale

MLOps | Machine Learning Operations — multi-cloud MLOps foundations
Applied Python Data Engineering — complementary Spark/Snowflake tooling
Large Language Model Operations (LLMOps) — deeper LLM production ops

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