This repository provides a comprehensive course for developers, data scientists, and product managers to learn how to build, deploy, and iterate on production-grade machine learning applications. It bridges the gap between ML experimentation and robust software engineering practices, enabling the creation of reliable ML-powered products.

How It Works

The course follows a first-principles approach, emphasizing software engineering best practices and scalability for ML workloads. It guides users through an iterative process, starting from experimentation (design, development) and moving to production (deployment, iteration), integrating MLOps components like tracking, testing, and serving. The framework is built on Python and leverages Ray for distributed computing, allowing seamless scaling without learning new languages.

Quick Start & Requirements

• Installation: Clone the repository (git clone https://github.com/GokuMohandas/Made-With-ML.git .) and set up a Python virtual environment (Python 3.10 recommended). Install dependencies with pip install -r requirements.txt.
• Prerequisites: Python 3.10, pre-commit. Optional: Anyscale account for cloud compute.
• Setup: Local setup involves cloning, creating a virtual environment, and installing requirements. Anyscale setup involves creating a workspace and cluster environment.
• Resources: Local execution is possible on a laptop, but Anyscale or cloud VMs are recommended for larger workloads.
• Links: Course Lessons: https://madewithml.com/

Highlighted Details

• End-to-end MLOps: Covers design, development, deployment, and iteration.
• Production-grade ML: Focuses on building reliable and scalable ML systems.
• CI/CD Integration: Demonstrates automated workflows using GitHub Actions and Anyscale.
• Scalability: Leverages Ray for distributed training, tuning, and serving.
• Experiment Tracking: Integrates MLflow for tracking experiments and models.

Maintenance & Community

The project is actively maintained by Goku Mohandas and has a large community of over 40,000 developers. Further community engagement can be found via links provided in the README.

Licensing & Compatibility

The repository's code is typically licensed under permissive licenses like MIT, allowing for commercial use and integration into closed-source projects. Specific license details should be verified within the repository.

Limitations & Caveats

While designed for production, achieving true production deployment requires setting up and managing cloud infrastructure (AWS, GCP, Azure) or Kubernetes, which can be complex. The course strongly recommends Anyscale for a managed experience.