Machine learning has rapidly evolved beyond experimental notebooks and isolated model development. Today, companies require production-grade ML systems that are reliable, scalable, maintainable, and continuously improving. However, bridging the gap between academic machine learning knowledge and real-world industry practices remains a challenge for many developers. This is where the Made With ML repository by Goku Mohandas stands out as one of the most comprehensive and practical resources for mastering the end-to-end ML lifecycle.
With more than forty thousand stars on GitHub, Made With ML has become a leading open-source learning platform that brings together first-principles thinking, software engineering best practices, and production-ready MLOps workflows. The repository is designed to help developers learn how to design, develop, deploy, and iterate on machine learning systems at scale. It teaches students, professionals, and organizations how to responsibly deliver ML value and build infrastructure that supports continuous improvement.
This blog explores the architecture, curriculum, and practical tools offered in the Made With ML repository, while highlighting why it has become a must-learn resource for anyone serious about production-level ML.
What Is the Made With ML Repository?
Made With ML is an open-source, project-based ML engineering curriculum created to teach developers how to build machine learning applications that work in real-world environments. Unlike materials that focus only on model training or theory, this repository goes deeper into:
- MLOps practices
- Reproducibility and experiment tracking
- CI/CD workflows
- Scalable training and serving
- Data quality and orchestration
- Deployment pipelines
- Testing and monitoring
- Infrastructure and automation
It covers the full ML lifecycle: design → development → deployment → iteration, using tools such as Ray, PyTorch, MLflow, GitHub Actions, Anyscale and more.
A First-Principles Approach to ML Engineering
One of the unique strengths of Made With ML is its emphasis on first principles. Instead of memorizing frameworks, learners gain clarity about:
- Why certain design choices matter
- How infrastructure supports ML workflows
- What separates experimental code from production systems
- When to focus on model performance vs. reliability
- Why reproducibility determines long-term health of ML projects
This mindset helps developers handle real-world ambiguity and build scalable systems rather than isolated scripts.
Designed for All Levels of Developers
The repository is intentionally accessible to a wide audience:
- Software engineers integrating ML into products
- Data scientists needing to learn software engineering
- Students preparing for industry roles
- Product managers and tech leaders developing technical literacy
Each lesson combines conceptual explanations, visual walkthroughs, clean implementation, and hands-on projects.
Setting Up the ML Environment
Made With ML provides instructions for:
- Local development
- Cloud VM development
- Anyscale Workspaces with GPU compute
Users learn how to configure clusters, manage Python environments, install dependencies, and organize their project structure. This section teaches the foundations for scalable and reproducible development.
Jupyter Notebooks for Core ML Workloads
The repository includes a comprehensive notebook that demonstrates core workloads such as:
- Data preprocessing
- Model training
- Evaluation
- Hyperparameter tuning
- Serving predictions
This allows learners to experiment interactively before transitioning to production-ready script versions.
Clean Python Scripts Following Industry Standards
A major highlight of the Made With ML repository is its refactored production code. The teaching notebook is converted into clean, modular Python scripts with strong engineering practices:
- config.py
- data.py
- models.py
- train.py
- tune.py
- evaluate.py
- predict.py
- serve.py
- utils.py
These scripts demonstrate:
- Logging
- Versioning
- Documentation
- Testing
- Distributed computing
- Modular design
- Reusability
Developers learn how to structure ML systems for long-term maintenance.
Training and Tuning at Scale
The repository teaches how to perform distributed training and tuning using Ray. With simple CLI commands, developers can run training jobs with configurable workers, CPUs, GPUs, and batch sizes.
Hyperparameter tuning is integrated using Ray Tune, enabling scalable experimentation and efficient model optimization.
Experiment Tracking with MLflow
Experiment tracking is essential in production ML. Made With ML demonstrates how to:
- Log metrics
- Track artifacts
- Store models
- Compare experiments
- Visualize training curves
- Retrieve best-performing runs
The repository teaches how to run MLflow locally or through cloud services, building skills required for team collaboration and long-term model governance.
Model Serving and Inference
Serving models is a core part of production ML. The repository demonstrates:
- Local model serving
- Ray Serve for scalable deployment
- Inference via Python, HTTP, or cURL
This section teaches how to convert trained models into real-time inference services that power applications.
Testing and Quality Assurance
Made With ML emphasizes testing at multiple layers:
- Code-level unit tests
- Data tests ensuring quality
- Model behavior tests
- Comprehensive test coverage reports
This ensures the entire ML system, not just the model, is stable and predictable.
Deploying ML Systems Using Anyscale and Cloud Services
The repository guides developers through:
- Authentication
- Cluster environments
- Compute configurations
- Submitting distributed jobs
- Deploying scalable services
- Rolling back to previous versions
These sections help developers understand real production pipelines that integrate automation, compute scaling, and fault tolerance.
CI/CD Automation with GitHub Actions
Made With ML provides a full CI/CD framework, showing how to:
- Automate training jobs
- Deploy models automatically
- Trigger reviews and tests on every PR
- Roll out and roll back production services
- Manage credentials securely
This teaches industry-standard DevOps concepts applied to machine learning.
Conclusion
The Made With ML repository is one of the most practical and comprehensive resources for developers who want to build real-world machine learning systems. Its combination of first-principles teaching, hands-on implementation, production-grade workflows, and clean engineering practices make it uniquely valuable. Whether you are a student, data scientist, or engineer, this repository equips you with the skills required to design, develop, deploy, and iterate on machine learning systems at scale. With its emphasis on MLOps, infrastructure, reproducibility, and automation, Made With ML sets a new benchmark for learning how to deliver ML solutions responsibly and effectively.
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