Problem solved

Many ML practitioners know how to build models in notebooks but struggle to transition them into reliable, maintainable production services. Made With ML bridges this gap by offering a structured, end-to-end example that teaches the engineering principles and tools needed to serve models at scale, handle data drift, and implement CI/CD pipelines.

Use cases

• Learning how to design and implement a full ML system from scratch
• Setting up automated training pipelines with experiment tracking
• Deploying models as REST APIs using FastAPI and Docker
• Building a monitoring dashboard for model performance and data quality
• Iterating on models with A/B testing and shadow deployment techniques

Audience

• Developers
• ML Engineers
• Data Scientists

Pros

• Comprehensive end-to-end ML system demonstration
• Strong educational narrative with clear documentation
• Large community and star count indicating trust
• Uses modern tools like FastAPI, Ray, and Docker
• Promotes best practices for production ML

Cons

• Primarily a learning resource rather than a production-ready library
• Limited to a single example use case (text classification)
• May require adaptation for different domains or scale