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.

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

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.

Receipes of publicly-available Jupyter images

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

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

🦾 Accelerate ML Training and Experimentation in VSCode

Want to develop ML using vscode remote dev on a powerful GPU cluster running k8s? Then this repo is for you!

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.

This project aims to train the YOLOv7 object detection model on a custom dataset comprising diverse aquarium images containing fish and aquatic creatures.

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

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

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

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

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

interactive coding environment for microservices demo

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

The collaboration workspace for Machine Learning