README

 Made With ML
Design · Develop · Deploy · Iterate

Join 40K+ developers in learning how to responsibly deliver value with ML.
    





     
     
     
    
    
    🔥  Among the top ML repositories on GitHub





 Lessons

Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.

- Lessons: https://madewithml.com/
- Code: GokuMohandas/Made-With-ML


  


 Overview

In this course, we'll go from experimentation (design + development) to production (deployment + iteration). We'll do this iteratively by motivating the components that will enable us to build a reliable production system.


    Be sure to watch the video below for a quick overview of what we'll be building.



  




- 💡 First principles: before we jump straight into the code, we develop a first principles understanding for every machine learning concept.
- 💻 Best practices: implement software engineering best practices as we develop and deploy our machine learning models.
- 📈 Scale: easily scale ML workloads (data, train, tune, serve) in Python without having to learn completely new languages.
- ⚙️ MLOps: connect MLOps components (tracking, testing, serving, orchestration, etc.) as we build an end-to-end machine learning system.
- 🚀 Dev to Prod: learn how to quickly and reliably go from development to production without any changes to our code or infra management.
- 🐙 CI/CD: learn how to create mature CI/CD workflows to continuously train and deploy better models in a modular way that integrates with any stack.

 Audience

Machine learning is not a separate industry, instead, it's a powerful way of thinking about data that's not reserved for any one type of person.

- 👩‍💻 All developers: whether software/infra engineer or data scientist, ML is increasingly becoming a key part of the products that you'll be developing.
- 👩‍🎓 College graduates: learn the practical skills required for industry and bridge gap between the university curriculum and what industry expects.
- 👩‍💼 Product/Leadership: who want to develop a technical foundation so that they can build amazing (and reliable) products powered by machine learning.

 Set up

Be sure to go through the course for a much more detailed walkthrough of the content on this repository. We will have instructions for both local laptop and Anyscale clusters for the sections below, so be sure to toggle the ► dropdown based on what you're using (Anyscale instructions will be toggled on by default). If you do want to run this course with Anyscale, where we'll provide the structure, compute (GPUs) and community to learn everything in one day, join our next upcoming live cohort → sign up here!

 Cluster

We'll start by setting up our cluster with the environment and compute configurations.


  Local
  Your personal laptop (single machine) will act as the cluster, where one CPU will be the head node and some of the remaining CPU will be

Top issues

Latest release

Release prior to agentic workflow updates