This is the repository of my study in MLOps Zoomcamp from DataTalksClub.
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Updated
May 27, 2024 - Jupyter Notebook
This is the repository of my study in MLOps Zoomcamp from DataTalksClub.
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.
Receipes of publicly-available Jupyter images
🛠 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.
🦾 Accelerate ML Training and Experimentation in VSCode
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
Documents Participation in the MLOps ZoomCamp by Datatalks Club, showcasing various MLOps practices: Experiment Tracking, Orchestration, Deployment, Monitoring, and Best Practices.
Example end-to-end ml pipeline build with the Sagemaker Python SDK
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/
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