New Release! Git-backed Machine Learning Model Registry for all your model management needs.
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.
July '22 Heartbeat
What a couple months it's been! This month you will find lots that's happening in IRL conferences again and in case you missed it, we've had some new, exciting releases! In addtion you'll find a guide to migrate from Git-LFS to DVC, the first Community piece on MLEM, and demo videos. Welcome to July!
Syncing Data to GCP Storage Buckets
We're going to set up an GCP storage bucket remote in a DVC project.
Turn Visual Studio Code into a machine learning experimentation platform with the DVC extension
Today we are releasing the DVC extension, which brings a full ML experimentation platform to Visual Studio Code.
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.
May '22 Heartbeat
Monthly updates are here! You will find a link to Chip Huyen's new book, great guides and frameworks on the ReciprocateX nature of AI, tons of company news, Dmitry on TFIR, beyond machine learning use cases and more! Welcome to May!
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.
