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    Tutorial

    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
    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, 20226 min read
    Preventing Stale Models in Production
    We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.
    • Milecia McGregor
    • Mar 31, 20227 min read
    Running Collaborative Experiments
    Sharing experiments with teammates can help you build models more efficiently.
    • Milecia McGregor
    • Dec 13, 20214 min read
    Adding Data to Build a More Generic Model
    You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.
    • Milecia McGregor
    • Oct 05, 20217 min read