Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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Updated
May 23, 2024 - Python
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Workflow Engine for Kubernetes
A high-throughput and memory-efficient inference and serving engine for LLMs
☁️ 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
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Free MLOps course from DataTalks.Club
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
Machine Learning Pipelines for Kubeflow
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"
An orchestration platform for the development, production, and observation of data assets.
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
MLOps using Azure ML Services and Azure DevOps
Standardized Serverless ML Inference Platform on Kubernetes
The Open Source Feature Store for Machine Learning
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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