A roundup of technical Q&A's from the DVC and CML communities. This month: working with CML and GCP, DVC data and remotes, DVC pipelines and setups, and more.

Monthly updates are here! You will find a link to Chip Huyen's new book, great guides and frameworks on the ReciprocateX nature of AI, tons of company news, Dmitry on TFIR, beyond machine learning use cases and more! Welcome to May!

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.

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.

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.

A roundup of technical Q&As from the DVC and CML community. This month: CML updates, working with multiple datasets, using DVC stages, and more.

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.

Monthly updates are here! You will find the future of AI Infrastruture is modular, articles on distribution drift and how to solve it, the usual great tutorials and workflows from the Community, online course updates, new docs and more! Happy April!