A high-throughput and memory-efficient inference and serving engine for LLMs
Learn how to design, 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 developer platform to build AI/LLM applications and models with confidence. Enhance your AI applications with end-to-end tracking, observability, and evaluations, all in one integrated platform.
☁️ Build multimodal AI applications with cloud-native stack
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
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.
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
An orchestration platform for the development, production, and observation of data assets.
A curated list of references for MLOps
Free MLOps course from DataTalks.Club
Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
This repository delivers end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Always know what to expect from your data.
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