The Full Stack 7-Steps MLOps Framework

Learn MLE & MLOps for free by building, deploying and monitoring an end-to-end ML batch system.

—> source code + 2.5 hours of reading & video materials on Medium

This is the architecture of the ML system you will learn to build during the course.

ML System Batch Architecture

The course will teach you how to build a production-ready ML batch system. Its primary focus is to engineer a scalable system using MLOps good practices. You will implement an ML system for forecasting hourly energy consumption levels across Denmark.

You will learn how to build, train, serve, and monitor an ML system using a batch architecture. We will show you how to integrate an experiment tracker, a model registry, a feature store, Docker, Airflow, GitHub Actions and more!

—> Level: Intermediate to Advanced | This course targets MLEs who want to build end-to-end ML systems and SWEs who wish to transition to MLE.

If you are unsure if this course is for you, here is an article presenting a high-level overview of the series.

Check out this short video to see what you will build during the course.

—> Source code on GitHub

—> Reading & video lessons on Medium's TDS publication:

  • Lesson 1: Batch Serving. Feature Stores. Feature Engineering Pipelines.

  • Lesson 2: Training Pipelines. ML Platforms. Hyperparameter Tuning.

  • Lesson 3: Batch Prediction Pipeline. Package Python Modules with Poetry.

  • Lesson 4: Private PyPi Server. Orchestrate Everything with Airflow.

  • Lesson 5: Data Validation for Quality and Integrity using GE. Model Performance Continuous Monitoring.

  • Lesson 6: Consume and Visualize your Model's Predictions using FastAPI and Streamlit. Dockerize Everything

  • Lesson 7: Deploy All the ML Components to GCP. Build a CI/CD Pipeline Using Github Actions.

  • Bonus Lesson: Behind the Scenes of an 'Imperfect' ML Project — Lessons and Insights

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Hands-On LLMs