Project Title

Made-With-ML — A comprehensive guide to designing, developing, deploying, and iterating on production-grade ML applications

Overview

Made-With-ML is an open-source project aimed at teaching developers how to responsibly deliver value with machine learning. It covers the entire lifecycle of ML applications, from experimentation to production, and emphasizes best practices in software engineering and MLOps. The project stands out for its practical approach, focusing on real-world applications and providing a solid foundation for building reliable ML systems.

Key Features

First principles understanding of machine learning concepts
Implementation of software engineering best practices in ML development and deployment
Scalability of ML workloads in Python without learning new languages
Integration of MLOps components for end-to-end machine learning systems
Guidance on transitioning from development to production without code or infrastructure changes
Mature CI/CD workflows for continuous training and deployment of ML models

Use Cases

Software and infrastructure engineers looking to incorporate ML into their products
Data scientists seeking to improve their software engineering skills for ML deployment
Product managers and leaders needing a technical foundation to build reliable ML-powered products

Advantages

Comprehensive coverage of the ML application lifecycle
Focus on practical skills and real-world applications
Emphasizes best practices in software engineering and MLOps
Caters to a wide audience, including developers, college graduates, and product/leadership

Limitations / Considerations

The project is教程-based, which may require a time commitment to fully benefit from the content
The depth of the material may be overwhelming for beginners without a strong background in ML or software engineering

TensorFlow: A popular open-source machine learning framework that provides a wide range of tools for building and deploying ML models. TensorFlow is more focused on the technical aspects of ML model development.
Scikit-learn: A simple and efficient tool for data mining and data analysis. It provides a range of supervised and unsupervised learning algorithms via a consistent interface. Scikit-learn is more focused on the algorithmic side of ML.
MLflow: An open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. MLflow is more specialized in the deployment and management aspects of ML applications.

Basic Information

GitHub: https://github.com/GokuMohandas/Made-With-ML
Stars: 42,116
License: Unknown
Last Commit: 2025-08-20

📊 Project Information

Project Name: Made-With-ML
GitHub URL: https://github.com/GokuMohandas/Made-With-ML
Programming Language: Jupyter Notebook
⭐ Stars: 42,116
🍴 Forks: 6,562
📅 Created: 2018-11-05
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

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📚 Documentation

Anyscale Workspace

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Made-With-ML

Project Description