Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

 Made With ML

Applied ML · MLOps · Production
Join 30K+ developers in learning how to responsibly deliver value with ML.

🔥  Among the top MLOps repositories on GitHub


Learn the foundations of machine learning through intuitive explanations, clean code and visualizations.

🛠  Toolkit 🔥  Machine Learning 🤖  Deep Learning
Notebooks Linear Regression CNNs
Python Logistic Regression Embeddings
NumPy Neural Network RNNs
Pandas Data Quality Attention
PyTorch Utilities Transformers

MLOps course

Learn how to combine machine learning with software engineering to build production-grade applications.

🎨  Design 💻  Developing  ♻️  Reproducibility
Product Packaging Git
Engineering Organization Pre-commit
Project Logging Versioning
🔢  Data Documentation Docker
Exploration Styling 🚀  Production
Labeling Makefile Dashboard
Preprocessing 📦  Serving CI/CD
Splitting Command-line Monitoring
Augmentation RESTful API Systems design
📈  Modeling   Testing ⎈  Data engineering
 Baselines Code Data stack
Evaluation Data Orchestration
Experiment tracking Models Feature store


ML is not a separate industry, instead, it's a powerful way of thinking about data, so let's make sure we have a solid foundation before we start changing the world with it. Made With ML is our medium to catalyze this goal and though we're off to great start, we still have a long way to go.

Who is this content for?

  • Software engineers looking to learn ML and become even better software engineers.
  • Data scientists who want to learn how to responsibly deliver value with ML.
  • College graduates looking to learn the practical skills they'll need for the industry.
  • Product Managers who want to develop a technical foundation for ML applications.

What makes this content unique?

  • hands-on: If you search production ML or MLOps online, you'll find great blog posts and tweets. But in order to really understand these concepts, you need to implement them. Unfortunately, you don’t see a lot of the inner workings of running production ML because of scale, proprietary content & expensive tools. However, Made With ML is free, open and live which makes it a perfect learning opportunity for the community.
  • intuition-first: We will never jump straight to code. In every lesson, we will develop intuition for the concepts and think about it from a product perspective.
  • software engineering: This course isn't just about ML. In fact, it's mostly about clean software engineering! We'll cover important concepts like versioning, testing, logging, etc. that really makes something production-grade product.
  • focused yet holistic: For every concept, we'll not only cover what's most important for our specific task (this is the case study aspect) but we'll also cover related methods (this is the guide aspect) which may prove to be useful in other situations.

Who is the author?

  • I've built and deployed large scale ML systems at Apple, as well as smaller systems with constraints at startups. I currently work closely with early-stage and F500 companies in helping them deliver value with ML while diving into the best and bespoke practices of this rapidly evolving space. I want to share this knowledge with the rest of the world so we can accelerate progress in this space.
  • Connect with me on Twitter and LinkedIn

Why is this free?

While this content is for everyone, it's especially targeted towards people who don't have as much opportunity to learn. I believe that creativity and intelligence are randomly distributed while opportunities are siloed. I want to enable more people to create and contribute to innovation.

To cite this content, please use:
    author       = {Goku Mohandas},
    title        = {Made With ML},
    howpublished = {\url{}},
    year         = {2022}


Learn how to responsibly develop, deploy and maintain production machine learning applications.




Code of conduct