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

The collaboration workspace for Machine Learning

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.

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

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

Receipes of publicly-available Jupyter images

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

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.

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

interactive coding environment for microservices demo

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

Coretex extension for VS Code, facilitating easier dev workflow by automating MLOps directly in your favorite IDE.

This is the repository of my study in MLOps Zoomcamp from DataTalksClub.

Documents Participation in the MLOps ZoomCamp by Datatalks Club, showcasing various MLOps practices: Experiment Tracking, Orchestration, Deployment, Monitoring, and Best Practices.

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.

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