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

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

A high-throughput and memory-efficient inference and serving engine for LLMs

☁️ Build multimodal AI applications with cloud-native stack

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

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.

Workflow Engine for Kubernetes

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

A curated list of references for MLOps

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

Machine Learning Engineering Open Book

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​.

Free MLOps course from DataTalks.Club

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

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

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.

Always know what to expect from your data.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"