Tutorial

One command to serve CV models from your laptop in the cloud 🚀

Got your data and model versioning down? ✅ Learn how to take your projects to the next level by creating a model registry right in your project's Git repo

In this guide we will take a Jupyter Notebook and use Papermill to turn it into a simple, one-stage DVC pipeline.

Use CML from a Bitbucket pipeline to provision an AWS EC2 instance and (re)train a machine learning model.

Once you have a machine learning model that's ready for production, getting it out can be complicated. In this tutorial, we're going to use MLEM to deploy a model as a web API.

We're going to set up a GCP storage bucket remote in a DVC project.

We're going to set up an Azure Blob Storage remote in a DVC project.

We're going to set up an AWS S3 remote in a DVC project.

Tutorial for easily running experiments in the cloud with the help of Terraform Provider Iterative (TPI) and Docker.