There are 3 repositories under dvc topic.
Generic template to bootstrap your PyTorch project.
🛠 MLOps end-to-end guide and tutorial website, using IBM Watson, DVC, CML, Terraform, Github Actions and more.
Machine learning experiment tracking and data versioning with DVC extension for VS Code
Get started DVC project
All the available resources to master MLOPS from scratch
Digital Image Correlation & Digital Volume Correlation Library
A list of projects relying on Iterative.AI tools to achieve awesomeness
Dataset registry DVC project
This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app.
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
Get started DVC project
Awesome MLOps Course Outline
A use case of a reproducible machine learning pipeline using Dask, DVC, and MLflow.
Source code and generator scripts for example DVC projects
🍪 Cookiecutter template for MLOps Project. Based on: https://mlops-guide.github.io/
Reading time from analog clocks
Implementation of USAD (UnSupervised Anomaly Detection on multivariate time series) in PyTorch Lightning
Tutorial on experiment tracking and reproducibility for Machine Learning projects with DVC
Open-source 3D Model datasets
Building a maintainable Machine Learning pipeline using DVC
❓ DVC Studio Issues, Question, and Discussions
A simple yet complete guide to MLOps tools and practices - from a conventional way to a modern approach of working with ML projects.
This project contains the production ready Machine Learning(Deep Learning) solution for detecting and classifying the brain tumor in medical images
Workshop about DVC VSCode Extension
Harness Large Language Models like OpenAI's GPT-3.5 for data annotation and model enhancement. This framework combines human expertise with LLM precision, employs Iterative Active Learning for continuous improvement, and integrates CleanLab (Confident Learning) to ensure high-quality datasets and superior model performance