There are 2 repositories under mlops-environment topic.
🛠 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
Receipes of publicly-available Jupyter images
Example end-to-end ml pipeline build with the Sagemaker Python SDK
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
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.
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.
Small test to see how MLFLOW relates to experiment tracking with Streamlit
🦾 Accelerate ML Training and Experimentation in VSCode
Documents Participation in the MLOps ZoomCamp by Datatalks Club, showcasing various MLOps practices: Experiment Tracking, Orchestration, Deployment, Monitoring, and Best Practices.
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.