Product updates, news, tutorials, integrations, and deep dives.
Release
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.
Git-backed Machine Learning Model Registry to bring order to chaos
🚀 As Machine Learning projects and teams grow, keeping track of all the models and their production status gets increasingly complex. Iterative Studio's Git-backed Model Registry solves this.
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.
Productionize your models with MLEM in a Git-native way
Introducing MLEM - one tool to run your models anywhere.
Machine Learning Workloads with Terraform Provider Iterative
Today we introduce painless resource orchestration for your machine learning projects in conjunction with HashiCorp Terraform.
Don't Just Track Your ML Experiments, Version Them
ML experiment versioning brings together the benefits of traditional code versioning and modern day experiment tracking, super charging your ability to reproduce and iterate on your work.
Easy Stuctural Refactors to Python Source Code
Simple, hassle-free, dependency-free, AST based source code refactoring toolkit.
Introducing DVC Studio
🚀 We are excited to release DVC Studio, the online UI for DVC and CML. Use DVC Studio for ML versioning, visualization, teamwork and no-code automation on top of DVC and Git. Read all about the exciting features and watch videos to get started quickly.
Git Custom References for ML Experiments
In DVC 2.0, we’ve introduced a new feature set aimed at simplifying the versioning of lightweight ML experiments. In this post, we’ll dive into how exactly these new experiments work.
