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MLOps

Deploy Computer Vision Models Faster and Easier
One command to serve CV models from your laptop in the cloud 🚀
  • Mike Sveshnikov
  • Jan 19, 20233 min read
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
  • Sep 06, 20225 min read
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.
  • Tapa Dipti Sitaula
  • Jul 26, 20224 min read
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.
  • Milecia McGregor
  • Jul 19, 20225 min read
Syncing Data to GCP Storage Buckets
We're going to set up a GCP storage bucket remote in a DVC project.
  • Milecia McGregor
  • Jul 06, 20224 min 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
  • Jun 14, 20223 min read
Syncing Data to Azure Blob Storage
We're going to set up an Azure Blob Storage remote in a DVC project.
  • Milecia McGregor
  • Jun 13, 20224 min read
Productionize your models with MLEM in a Git-native way
Introducing MLEM - one tool to run your models anywhere.
  • Alexander Guschin
  • Jun 01, 20225 min read
Syncing Data to AWS S3
We're going to set up an AWS S3 remote in a DVC project.
  • Milecia McGregor
  • May 31, 20223 min read