🏕️ Reproducible development environment

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.

💻 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

🛠 MLOps end-to-end guide and tutorial website, using IBM Watson, DVC, CML, Terraform, Github Actions and more.

A simple guide to MLOps through ZenML and its various integrations.

Fast model deployment on any cloud 🚀

Azure MLOps

Tutorials on creating a reproducible and maintainable data science project

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

A beginner's project on automating the training, evaluation, versioning, and deployment of models using GitHub Actions.

The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸

Source of the FSDL 2022 labs, which are at https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022-labs

Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.

This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

MLOps Workshop using Weights and Bias (Wandb) and Github Actions.