Applied ML · MLOps · Production
Join 20K+ developers in learning how to responsibly deliver value with applied ML.
Join 20K+ developers in learning how to responsibly deliver value with applied ML.
Learn the foundations of ML through intuitive explanations, clean code and visuals.
| 🔢 Basics | 📈 Modeling | 🤖 Deep Learning |
| Notebooks | Linear Regression | CNNs |
| Python | Logistic Regression | Embeddings |
| NumPy | Neural Network | RNNs |
| Pandas | Data Quality | Attention (TBD) |
| PyTorch | Utilities | Transformers (TBD) |
📆 more topics coming soon!
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Learn how to apply ML to build a production grade product to deliver value.
Code: GokuMohandas/applied-ml
| 📦 Product | 🔢 Data | 📈 Modeling |
| Objective | Annotation | Baselines |
| Solution | Exploratory data analysis | Experiment tracking |
| Evaluation | Splitting | Optimization |
| Iteration | Preprocessing |
| 📝 Scripting | (cont.) | 📦 Application | ✅ Testing |
| Organization | Styling | Command line | Code |
| Packaging | Makefile | RESTful API | Data |
| Documentation | Logging | Model |
| ⏰ Version control | 🚀 Production | (cont.) |
| Git | Dashboard | Serving |
| Precommit | Docker | Feature stores |
| Versioning | CI/CD | Workflow management |
| Monitoring | Active learning |
📆 new lesson every week!
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- ML developers who want to become end-to-end ML developers.
- Software engineers who want to responsibly deliver value with applied ML.
- Product managers who want to have a comprehensive understanding of applied ML.
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.
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.
- 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's currently used by over 20K monthly active users.
- Connect with me on Twitter and LinkedIn
While this content is for everyone, it's especially targeted towards people who don't have as much opportunity to learn. I firmly believe that creativity and intelligence are randomly distributed but opportunity is siloed. I want to enable more people to create and contribute to innovation.