DVC AI Blog

Introducing MLEM - one tool to run your models anywhere.

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

A roundup of technical Q&A's from the DVC and CML communities. This month: working with CML and GCP, DVC data and remotes, DVC pipelines and setups, and more.

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

Monthly updates are here! You will find a link to Chip Huyen's new book, great guides and frameworks on the iterative nature of AI, tons of company news, Dmitry on TFIR, beyond machine learning use cases and more! Welcome to May!

Tutorial for easily moving a local ML experiment to a remote cloud machine with the help of Terraform Provider Iterative (TPI).

In this final part, we will focus on leveraging cloud infrastructure with CML; enabling automatic reporting (graphs, images, reports and tables with performance metrics) for PRs; and the eventual deployment process.

Use CML to automatically retrain a model on a provisioned AWS EC2 instance and export the model to a DVC remote storage on Google Drive.

In part 1, we talked about effective management and versioning of large datasets and the creation of reproducible ML pipelines. Here we'll learn about experiment management: generation of many experiments by tweaking configurations and hyperparameters; comparison of experiments based on their performance metrics; and persistence of the most promising ones