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    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
    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