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    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
    Using Experiments for Transfer Learning
    You can work with pretrained models and fine-tune them with DVC experiments.
    • Milecia McGregor
    • Aug 24, 202112 min read
    Tuning Hyperparameters with Reproducible Experiments
    Using DVC, you'll be able to track the changes that give you an ideal model.
    • Milecia McGregor
    • Jul 19, 20218 min read
    Cloud Data Sync Methods and Benchmark: DVC vs Rclone
    DVC 1.0 optimized data synchronization to and from remote storage. Here's how we did it.
    • Peter Rowlands
    • Nov 26, 202013 min read