Git
Syncing Data to AWS S3
We're going to set up an AWS S3 remote in a DVC project.
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.
May '22 Heartbeat
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!
Moving Local Experiments to the Cloud with Terraform Provider Iterative (TPI)
Tutorial for easily moving a local ML experiment to a remote cloud machine with the help of Terraform Provider Iterative (TPI).
Training and saving models with CML on a dedicated AWS EC2 runner (part 2)
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.
Machine Learning Workloads with Terraform Provider Iterative
Today we introduce painless resource orchestration for your machine learning projects in conjunction with HashiCorp Terraform.
Training and saving models with CML on a self-hosted AWS EC2 runner (part 1)
In this guide we will show how you can use CML to automatically retrain a model and save its outputs to your Github repository using a provisioned AWS EC2 runner.
April '22 Heartbeat
Monthly updates are here! You will find the future of AI Infrastruture is modular, articles on distribution drift and how to solve it, the usual great tutorials and workflows from the Community, online course updates, new docs and more! Happy April!
Preventing Stale Models in Production
We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.