Experiment Tracking

DVC extension enables you to run, track and manage ML experiments without leaving VS Code.

DVC 3.0 introduces a stack of tools outside the command line to bring it closer to where you work (in code, IDE, web) while also focusing on DVC fundamentals.

Experiment tracking in DVC with a few lines of Python.

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.

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

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.

ML experiment versioning brings together the benefits of traditional code versioning and modern day experiment tracking, super charging your ability to reproduce and iterate on your work.