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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
May '22 Community Gems
A roundup of technical Q&A's from the DVC and CML communities. This month: working with CML and GCP, DVC data and remotes, DVC pipelines and setups, and more.
  • Milecia McGregor
  • May 26, 20223 min read
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.
  • Casper da Costa-Luis
  • May 24, 20223 min read
May '22 Heartbeat
Monthly updates are here! You will find a link to Chip Huyen's new book, great guides and frameworks on the iterative nature of AI, tons of company news, Dmitry on TFIR, beyond machine learning use cases and more! Welcome to May!
  • Jeny De Figueiredo
  • May 16, 20228 min read
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).
  • Maria Khalusova
  • May 12, 20227 min read
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.
  • Alex Kim
  • May 09, 20226 min read
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.
  • Rob de Wit
  • May 06, 20226 min read
End-to-End Computer Vision API, Part 2: Local Experiments
In part 1, we talked about effective management and versioning of large datasets and the creation of reproducible ML pipelines. Here we'll learn about experiment management: generation of many experiments by tweaking configurations and hyperparameters; comparison of experiments based on their performance metrics; and persistence of the most promising ones
  • Alex Kim
  • May 05, 20225 min read
End-to-End Computer Vision API, Part 1: Data Versioning and ML Pipelines
In most cases, training a well-performing Computer Vision (CV) model is not the hardest part of building a Computer Vision-based system. The hardest parts are usually about incorporating this model into a maintainable application that runs in a production environment bringing value to the customers and our business.
  • Alex Kim
  • May 03, 20225 min read