🏕️ 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
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations
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 🚀
Tutorials on creating a reproducible and maintainable data science project
Azure MLOps
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
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.
MLOps Workshop using Weights and Bias (Wandb) and Github Actions.
Example project with a complete MLOps cycle: versioning data, generating reports on pull requests and deploying the model on releases with DVC and CML using Github Actions and IBM Watson. Part of the Engineering Final Project @ Insper
This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS). It addresses the basic implementation requirements as well as ways you can pack thousands of unique PyTorch deep learning (DL) models into a scalable architecture and evaluate performance
Add a description, image, and links to the mlops-workflow topic page so that developers can more easily learn about it.
To associate your repository with the mlops-workflow topic, visit your repo's landing page and select "manage topics."