LIVE WEBINAR · SEP 22

End-to-End Lineage with Amazon SageMaker AI, MLflow & DVC

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.
Rob de Wit
Rob de Wit
September 6, 2022
7 minutes read
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.
Rob de Wit
Rob de Wit
June 14, 2022
4 minutes read
Productionize your models with MLEM in a Git-native way
Introducing MLEM - one tool to run your models anywhere.
Alexander Guschin
Alexander Guschin
June 1, 2022
7 minutes read
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.
Dave Berenbaum
Dave Berenbaum
December 7, 2021
6 minutes read
Easy Structural Refactors to Python Source Code
Simple, hassle-free, dependency-free, AST based source code refactoring toolkit.
Batuhan Taskaya
Batuhan Taskaya
September 24, 2021
4 minutes read
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.
Peter Rowlands
Peter Rowlands
April 19, 2021
8 minutes read