In the high-stakes world of machine learning, there is a notorious “valley of death” between a model that works on a data scientist’s laptop and one that actually delivers value in production. Most online courses focus on the former—teaching you how to optimize a loss function or pick the right architecture. But as any veteran engineer will tell you, the model is often the easiest part. The real challenge lies in the Operations (MLOps).
Enter MadeWithML, an open-source educational powerhouse that has become the gold standard for learning how to bridge the gap between AI research and software engineering.
The “First Principles” Philosophy
Founded by Goku Mohandas (an industry veteran with experience at Apple and various high-growth startups), MadeWithML is built on a “First Principles” approach. Instead of overwhelming students with a “tool soup” of competing cloud services, the curriculum focuses on the underlying logic of production systems.
“Before we jump straight into the code, we develop a first principles understanding for every machine learning concept,” says Mohandas.
This philosophy ensures that whether you are using AWS, GCP, or on-premise servers, the engineering rigor remains the same. It’s about building systems that are reproducible, testable, and scalable.
A Comprehensive 8-Step Roadmap
The curriculum is structured into eight distinct phases, taking a project from an initial idea to a live, monitored service:
| Phase | Focus Areas |
| 1. Design | Product mindset, system design, and setting up the development environment. |
| 2. Data | Labeling, exploration (EDA), preprocessing, and handling distributed workloads. |
| 3. Model | Training, experiment tracking (MLflow), and hyperparameter tuning. |
| 4. Develop | Scripting, command-line interfaces, and clean code practices. |
| 5. Utilities | Logging, documentation, and pre-commit hooks for quality control. |
| 6. Test | Writing unit and integration tests specifically for ML code, data, and models. |
| 7. Reproducibility | Versioning data and code to ensure every experiment can be recreated. |
| 8. Production | CI/CD workflows, serving (APIs), and monitoring for data/model drift. |
Why It Matters for LLMOps
As we move into 2026, the industry is shifting toward LLMOps (Large Language Model Operations). While the artifacts have changed—we now track prompts and vector embeddings instead of just weights—the core engineering principles taught at MadeWithML remain identical.
Whether you are fine-tuning a small BERT model or deploying a complex RAG (Retrieval-Augmented Generation) system with a billion-parameter LLM, you still need to version your data, test your outputs, and monitor for “hallucinations” or drift. MadeWithML provides the foundational “hooks” to hang these new concepts on.
100% Free and Open-Source
Perhaps the most remarkable thing about MadeWithML is its commitment to open education. The entire course—including 49 lessons and a massive GitHub repository—is completely free.
For developers looking to transition into AI, or data scientists looking to level up their engineering skills, it offers a project-based approach: you don’t just learn theory; you build a platform that discovers and categorizes ML content from around the web.
Get Involved:
- Website: madewithml.com
- GitHub: GokuMohandas/Made-With-ML
- Community: Join over 40,000 developers already following the roadmap.
