mlops

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

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Label Studio is a multi-type data labeling and annotation tool with standardized output format

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

☁️ Build multimodal AI applications with cloud-native stack

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Turns Data and AI algorithms into production-ready web applications in no time.

Machine Learning Engineering Open Book

The absolute trainer to light up AI agents.

Workflow Engine for Kubernetes

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

An orchestration platform for the development, production, and observation of data assets.

Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort

A curated list of references for MLOps

Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.

Always know what to expect from your data.

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.

Build, Manage and Deploy AI/ML Systems

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Dynamic, resilient AI orchestration. Coordinate data, models, and compute as you build AI workflows.

The Open Source Feature Store for AI/ML

Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..