Welcome to Machine Learning REPA Library¶
Welcome to the community-driven Machine Learning REPA Library, your go-to resource for MLOps and ML Engineering solutions. This library is dedicated to providing the most up-to-date and relevant information, tutorials, and solutions to help you succeed in your machine learning projects.
We're committed to empowering the ML community with the tools and knowledge necessary to tackle even the most challenging ML projects. Join us in our mission to advance the field of ML and build a community of successful and skilled ML practitioners
For more information about the community visit mlrepa.org.
As an ML/MLOps engineer, it is crucial to embrace the REPA principles, which are fundamental to ML engineering and MLOps design. Let's explore each of these principles:
|⚛️ Reliable ML||R: Ensuring reliability and reproducibility of ML workflows and experiments||Reproducibility, ML System Design, ML Interpretability, A/B testing, etc.|
|🧪 Experiments Management||E: Tracking and managing experiments, including data, hyperparameters, and metrics||Metrics Tracking, Experiment Versioning, Model Lifecycle Management, etc.|
|🛠️ Pipelines||P: Designing end-to-end ML pipelines for data ingestion, preprocessing, model training, and deployment||Production ML, Continuous Training, Data Pipelines, CI/CD pipelines, Monitoring pipelines, etc.|
|🤖 Automation||A: Automating repetitive tasks and implementing CI/CD practices||Automate ML and Data pipelines, AutoML, CI/CD, etc.|
We hope you'll enjoyed tutorials and learn a lot of useful techniques.
If you need any assistance, please feel free to create a GitHub issue with your question. We are always here to help and will get back to you as soon as possible. 👍
Contribute to the community! 🙏🏻
You may be wondering how you can help the community. Here are some ways you can contribute:
- ⭐ Put a star on our ML REPA library repository on GitHub
- 📣 Share our tutorials with friends and colleagues!
- ✅ Fill out the Feedback Form We would appreciate any suggestions or comments you may have
- ✍️ Join us to write tutorials! If you have a tutorial or just an idea, please feel free to create a GitHub issue with your suggestion. We will get back to you as soon as possible.
Thank you for taking the time to help the community! 👍