mlops-environment

The collaboration workspace for Machine Learning

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

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

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

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

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

🍪 Cookiecutter template for MLOps Project. Based on: https://mlops-guide.github.io/

Reference code base for ML Engineering in Action, Manning Publications Author: Ben Wilson

Spring 25 - Artificial Intelligence

Receipes of publicly-available Jupyter images

Example end-to-end ml pipeline build with the Sagemaker Python SDK

One environment. Infinite packages. Zero conflicts.

Project Includes python script (which runs in an MLOps environment) with the task of auto training Models until a desired accuracy is achieved.

Gaussian Time Series model and MLOps pipeline using the AWS to deploy the model in a production environment.

GitHub Actions is a continuous integration and continuous delivery [CI/CD] platform that allows you to automate your build, test, and deployment pipeline. You can create workflows that build and test every pull request to your repository, or deploy merged pull requests to production.

This repository demonstrates how to set up automated model training workflows triggered by AWS S3 using Kestra. When new customer interaction data is added to S3, the system retrains recommendation models to enhance personalization. Configuring environment variables with GitHub and AWS credentials.

interactive coding environment for microservices demo

Some examples of running R in a Docker container with machine learning and MLOps features