MLOps & Data Engineering

Build the Systems That Power AI

A hands-on, plain-English guide to getting data and models into production: pipelines, Airflow, Spark, warehouses and lakes, streaming, Docker, the cloud, monitoring, and MLOps. Advanced infrastructure explained simply, one building block at a time.

Intermediate18 modules12-16 weeksDevelopers and analysts moving into data & ML engineering

Curriculum Modules

18 modules covering everything from fundamentals to advanced topics.

Capstone Project

Put everything together in a comprehensive final project that demonstrates your mastery. Includes real-world problem solving, documentation, and portfolio-ready deliverables.

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MLOps & Data Engineering

Curriculum Modules

Module 1: What Is Data Engineering & MLOps?

Module 2: Data Engineering Fundamentals

Module 3: Databases & SQL Basics

Module 4: ETL & ELT Pipelines

Module 5: Orchestration with Airflow

Module 6: Apache Spark & Big Data

Module 7: Data Warehouses

Module 8: Data Lakes & Lakehouses

Module 9: Stream Processing & Kafka

Module 10: Data Quality & Governance

Module 11: Docker & Containers

Module 12: Cloud Data Platforms

Module 13: Deploying ML Models

Module 14: ML Pipelines & CI/CD

Module 15: Monitoring & Observability

Module 16: Capstone — Build a Data Pipeline

Module 17: Glossary of Data & MLOps Terms

Module 18: Your Roadmap & Next Steps

Capstone Project