Made With ML
Applied ML · MLOps · Production
Join 30K+ developers in learning how to responsibly deliver value with ML.
Foundations
Learn the foundations of ML through intuitive explanations, clean code and visuals.
| Notebooks | Linear Regression | CNNs |
| Python | Logistic Regression | Embeddings |
| NumPy | Neural Network | RNNs |
| Pandas | Data Quality | Attention |
| PyTorch | Utilities | Transformers |
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MLOps
Learn how to apply ML to build a production grade product to deliver value.
- Lessons: https://madewithml.com/courses/mlops/
- Code: GokuMohandas/MLOps
| Product | Packaging | Git |
| System design | Organization | Pre-commit |
| Project | Logging | Versioning |
| Styling | Docker | |
| Labeling | Makefile | |
| Preprocessing | Documentation | Dashboard |
| Exploratory data analysis | CI/CD workflows | |
| Splitting | Command-line | Infrastructure |
| Augmentation | RESTful API | Monitoring |
| Feature store | ||
| Evaluation | Code | Pipelines |
| Experiment tracking | Data | Continual learning |
| Optimization | Models |
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FAQ
Who is this content for?
Software engineerslooking to learn ML and become even better software engineers.Data scientistswho want to learn how to responsibly deliver value with ML.College graduateslooking to learn the practical skills they'll need for the industry.Product Managerswho want to develop a technical foundation for ML applications.
What is the structure?
Lessons will be released weekly and each one will include:
intuition: high level overview of the concepts that will be covered and how it all fits together.code: simple code examples to illustrate the concept.application: applying the concept to our specific task.extensions: brief look at other tools and techniques that will be useful for difference situations.
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.
- 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:
@misc{madewithml,
author = {Goku Mohandas},
title = {Made With ML},
howpublished = {\url{https://madewithml.com/}},
year = {2021}
}