New Release! The DVC 3.0 Stack: Beyond the Command Line
Experiment Tracking
The DVC 3.0 Stack: Beyond the Command Line
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.
Instant Experiment Tracking: Just Add DVC!
Experiment tracking in DVC with a few lines of Python.
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.
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
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.
Don't Just Track Your ML Experiments, Version Them
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.
