Learn how to responsibly develop, deploy and maintain production machine learning applications.
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Updated
Apr 6, 2023 - Jupyter Notebook
Learn how to responsibly develop, deploy and maintain production machine learning applications.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
A curated list of references for MLOps
A Python framework for creating reproducible, maintainable and modular data science code.
Always know what to expect from your data.
Example
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 - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud https://cloud.qdrant.io/
Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai
Free MLOps course from DataTalks.Club
Unified Model Serving Framework
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.
ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Feature Store for Machine Learning
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