Learn how to design, develop, deploy and iterate on production-grade ML applications.
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Updated
Dec 7, 2023 - Jupyter Notebook
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
☁️ Build multimodal AI applications with cloud-native stack
Qdrant - High-performance, massive-scale Vector Database 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
A high-throughput and memory-efficient inference and serving engine for LLMs
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.
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.
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.
Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
Machine Learning Engineering Open Book
Free MLOps course from DataTalks.Club
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
Operating LLMs in production
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
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