Made With ML is an educational course and repository that teaches how to develop, deploy and iterate on production-grade machine learning applications.
The course addresses the gap between ML experimentation and production systems by combining machine learning with software engineering principles. It takes a first-principles approach to understanding ML concepts while implementing software engineering best practices throughout the development lifecycle. The material covers the full journey from design and development through deployment and iteration, with emphasis on building reliable systems that can scale ML workloads in Python without requiring new languages.
The course suits developers and data scientists who want to move beyond notebooks and experimentation into production-ready systems. It is particularly valuable for those building end-to-end ML systems who need to understand how to integrate MLOps components like tracking, testing, serving, and orchestration. The material emphasizes practical implementation of CI/CD workflows for continuous model training and deployment, making it relevant for teams that need to reliably move from development to production without code or infrastructure changes.
The project maintains active engagement with a large developer community through its website and social channels. The repository contains Jupyter Notebooks alongside comprehensive lessons that cover data engineering, data quality, distributed training, and deployment patterns. The course structure progresses systematically through design, development, deployment, and iteration phases, with code examples demonstrating concepts across machine learning frameworks and tools including PyTorch and Ray for distributed workloads.
