Applied ML · MLOps · Production

Join 20K+ developers in learning how to responsibly deliver value with ML.

## Features

Intuition-first
We'll never jump straight to code, instead we'll develop an intuition for the concepts first.
Hands-on
We won't just learn about MLOps concepts but also implement everything in code.
Engineering
It's not just about ML. It's about clean software engineering to create reliable products.
Comprehensive
We'll cover related methods & tools which may be useful in other situations.

## Basics

Learn the foundations of ML through intuitive explanations, clean code and visuals. GokuMohandas/madewithml → 🔥 Among the top ML repos on GitHub

🔢 Basics
🤖 Deep Learning

## MLOps

Learn how to apply ML to build a production grade product and deliver value. GokuMohandas/mlops → New lessons every month!

📦 Product 🔢 Data 📈 Modeling
📝 Scripting 📦 Application Testing
♻️ Reproducibility 🚀 Production
• Dashboard
• CI/CD
• Monitoring
• Feature stores
• Workflows

### Who is this content for?

• ML developers who want to become end-to-end ML developers.
• Software engineers who want to responsibly deliver value with ML.
• Product managers who want to have a comprehensive understanding of MLOps.

### 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 deployed large scale ML systems at Apple as well as smaller systems with constraints at startups and want to share the common principles I've learned.
• I created the (old) Made With ML so that the community can explore, learn and build ML and I learned how to build it into an end-to-end product that was used by over 20K monthly active users (5K DAU).
 1 2 3 4 5 6 @misc{madewithml, title = "Made With ML", author = "Goku Mohandas", url = "https://madewithml.com/" year = "2021", }