mlops
26 repositories with 10k+ stars are tagged mlops.
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Learn how to develop, deploy and iterate on production-grade ML applications.
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Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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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/
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Label Studio is a multi-type data labeling and annotation tool with standardized output format
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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.
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☁️ Build multimodal AI applications with cloud-native stack
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End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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Machine Learning Engineering Open Book
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Turns Data and AI algorithms into production-ready web applications in no time.
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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.
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An orchestration platform for the development, production, and observation of data assets.
- 15
Free MLOps course from DataTalks.Club. Register here 👇🏼 to get notified about the next cohort
- 16microsoft/nniArchived
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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Run any open-source LLMs, such as DeepSeek and Llama, as OpenAI compatible API endpoint in the cloud.
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Always know what to expect from your data.
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tensorzero/tensorzeroArchivedTensorZero is an open-source LLMOps platform that unifies an LLM gateway, observability, evaluation, optimization, and experimentation.
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The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
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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.
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Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
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A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`
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