Data and Model versioning for Iterative ML process
-
Updated
Feb 15, 2019 - Python
Data and Model versioning for Iterative ML process
Get Started: MNIST tutorial for DVC
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Building a maintainable Machine Learning pipeline using DVC
Fraud detection via residual neural network. (+ DVC)
My solution to the SIIM-ISIC Melanoma Classification Challenge on Kaggle.
🐋 Template for ML projects using VSCode Development Containers
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.
TFRecords and Keras Model Checkpoint Versioning and Distribution with DVC
🍪 Cookiecutter template for MLOps Project. Based on: https://mlops-guide.github.io/
This project predicts the best features of wines that relate to wine quality. This MLOps pipeline uses self-hosted Github actions runners, Random Forest regression, pandas, seaborn, DVC and CML.
A customized cookiecutter for data projects. It initializes a boilerplate repo based on best practices and my preferences.
A Cookiecutter template for my LVSN workflow. Utilises DVC for artifact tracking and Docker for deploying experiments on the LVSN servers.
Add a description, image, and links to the dvc topic page so that developers can more easily learn about it.
To associate your repository with the dvc topic, visit your repo's landing page and select "manage topics."