#Mlops-workflow
Showing 17 of 17 repositories tagged #mlops-workflow, ranked by stars
🏕️ Reproducible development environment for humans and agents
MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
:computer: Learn to make machines learn so that you don't have to struggle to program them; The ultimate list
Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
A work in progress to build out solutions in Rust for MLOPs
Pybind11 bindings for Whisper.cpp
Azure MLOps
Tutorials on creating a reproducible and maintainable data science project
The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Source of the FSDL 2022 labs, which are at https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022-labs
Ultimate AWS Data & AI Platform: Real-time flight delay predictions with complete DE, DS, MLOps, Web App & Multi-Agent LLM - All deployed via CDK self-mutating pipelines
2 Lines of code to track ML experiments + EDA + check into Github
A Recommendation Engine API that can be used to recommend movies, music, games, manga, anime, comics, tv shows and books. Deployed using an AWS EC2 instance.
Artificial Intelligence Labs - Udemy