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    Data Version Control in Real Life

    We write about machine learning workflow. From data versioning and processing to model productionization. We share our news, findings, interesting reads, community takeaways.
    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, 202215 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, 20229 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, 20229 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, 20227 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, 20227 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, 20229 min read
    April '22 Community Gems
    A roundup of technical Q&As from the DVC and CML community. This month: CML updates, working with multiple datasets, using DVC stages, and more.
    • Milecia McGregor
    • Apr 28, 20225 min read
    Machine Learning Workloads with Terraform Provider Iterative
    Today we introduce painless resource orchestration for your machine learning projects in conjunction with HashiCorp Terraform.
    • Maria Khalusova
    • Apr 27, 20223 min read
    Training and saving models with CML on a self-hosted AWS EC2 runner (part 1)
    In this guide we will show how you can use CML to automatically retrain a model and save its outputs to your Github repository using a provisioned AWS EC2 runner.
    • Rob de Wit
    • Apr 26, 20228 min read