New Release! The DVC 3.0 Stack: Beyond the Command Line
Product updates, news, tutorials, integrations, and deep dives.
Tutorial
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.
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.
Syncing Data to GCP Storage Buckets
We're going to set up a GCP storage bucket remote in a DVC project.
Syncing Data to Azure Blob Storage
We're going to set up an Azure Blob Storage remote in a DVC project.
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.
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).
End-to-End Computer Vision API, Part 3: Remote Experiments & CI/CD For Machine Learning
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.
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.
