Made-With-ML open source analysis

Learn how to design, develop, deploy and iterate on production-grade ML applications.

Project overview

⭐ 45533 · Jupyter Notebook · Last activity on GitHub: 2024-08-18

GitHub: https://github.com/GokuMohandas/Made-With-ML

Why it matters for engineering teams

Made-With-ML addresses the practical challenges faced by engineering teams when building and maintaining production-grade machine learning applications. It provides a comprehensive open source tool for engineering teams focused on data science, machine learning, and AI engineering roles, helping them design, deploy, and iterate on models effectively. The project is mature and reliable, with a strong community and extensive documentation that support real-world use cases, including distributed training and MLOps workflows. However, it may not be the best fit for teams looking for a quick, out-of-the-box solution or those without the capacity to manage Jupyter Notebook-based workflows and custom integrations. Its focus on production readiness means it requires some setup and expertise to fully leverage its capabilities.

When to use this project

This project is a strong choice when your team needs a production ready solution that covers the entire ML lifecycle, from development to deployment and monitoring. Consider alternatives if you require a lightweight tool for rapid prototyping or prefer fully managed cloud services without self hosted options.

Team fit and typical use cases

Machine learning and AI engineering teams benefit most from Made-With-ML, typically using it to build scalable models and integrate them into production systems. It is well suited for teams working on products involving natural language processing, deep learning, and distributed training, where reliability and iteration speed are critical. The project supports complex pipelines and MLOps practices, making it valuable for teams managing full-stack ML applications in production environments.

Best suited for

Topics and ecosystem

data-engineering data-quality data-science deep-learning distributed-ml distributed-training llms machine-learning mlops natural-language-processing python pytorch ray

Activity and freshness

Latest commit on GitHub: 2024-08-18. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.