Learn how to responsibly develop, deploy and maintain production machine learning applications.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
A curated list of references for MLOps
A Python framework for creating reproducible, maintainable and modular data science code.
Always know what to expect from your data.
Example
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
An orchestration platform for the development, production, and observation of data assets.
Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai
Free MLOps course from DataTalks.Club
Unified Model Serving Framework
Qdrant - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud https://qdrant.to/cloud
ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Feature Store for Machine Learning
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Add a description, image, and links to the mlops topic page so that developers can more easily learn about it.
To associate your repository with the mlops topic, visit your repo's landing page and select "manage topics."