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    Tutorial

    Deploy Computer Vision Models Faster and Easier
    One command to serve CV models from your laptop in the cloud 🚀
    • Mike Sveshnikov
    • Jan 19, 20233 min read
    Building a GitOps ML Model Registry with DVC and GTO
    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
    • Alexander Guschin
    • Dec 07, 20229 min read
    From Jupyter Notebook to DVC pipeline for reproducible ML experiments
    In this guide we will take a Jupyter Notebook and use Papermill to turn it into a simple, one-stage DVC pipeline.
    • Rob de Wit
    • Oct 24, 20229 min read
    CML Cloud Runners for Model Training in Bitbucket Pipelines
    Use CML from a Bitbucket pipeline to provision an AWS EC2 instance and (re)train a machine learning model.
    • Rob de Wit
    • Sep 06, 20225 min read
    Serving Machine Learning Models with MLEM
    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.
    • Milecia McGregor
    • Jul 19, 20225 min read
    Syncing Data to GCP Storage Buckets
    We're going to set up a GCP storage bucket remote in a DVC project.
    • Milecia McGregor
    • Jul 06, 20224 min read
    Syncing Data to Azure Blob Storage
    We're going to set up an Azure Blob Storage remote in a DVC project.
    • Milecia McGregor
    • Jun 13, 20224 min read
    Syncing Data to AWS S3
    We're going to set up an AWS S3 remote in a DVC project.
    • Milecia McGregor
    • May 31, 20223 min read
    Moving Local Experiments to the Cloud with Terraform Provider Iterative (TPI) and Docker
    Tutorial for easily running experiments in the cloud with the help of Terraform Provider Iterative (TPI) and Docker.
    • Casper da Costa-Luis
    • May 24, 20223 min read