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.
| 🔢 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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Code: GokuMohandas/applied-ml
| 📦 Product | 🔢 Data | 📈 Modeling |
| Objective | Annotation | Baselines |
| Solution | Exploratory data analysis | Experiment tracking |
| Evaluation | Splitting | Optimization |
| Iteration | Preprocessing |
| 📝 Scripting | ✅ Testing | ⏰ Version control |
| OOPs | Testing (code) | Git |
| Formatting | Testing (data) | Precommit |
| Packaging | Testing (model) | Makefile |
| Logging | Versioning |
| 🛠 API | 🚀 Production | (cont.) |
| RESTful API | Dashboard | Monitoring |
| Databases | Docker | Active learning |
| Authentication | Serving | Feature stores |
| Documentation | CI/CD | Scaling |
📆 new lesson every week!
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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.
- 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 along the way.
- I created 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