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.

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

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

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.

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 is a simple webapp for wine quality prediction and involves MLOPs including DVC for model and data tracking and Github actions for CI-Cd workflows. The app is deployed on Heroku.

Testing ZenML

Testing Kedro

🚀 Streamline your machine learning operations with a modular MLOps workflow for training, inference, and deployment using FastAPI, MLflow, MinIO, and PostgreSQL.

Testing flyte

Small test to see how MLFLOW relates to experiment tracking with Streamlit

🚀 Build and deploy machine learning models with this MLOps project template, designed for easy integration and effective monitoring in production environments.