CML

A roundup of technical Q&A's from the DVC community. This month: explaining DVC versioning mechanism, some tricks with pipelines and CML action, visualizing plots in VS Code extension.

A roundup of technical Q&A's from the DVC community. This month: deploying models MLEM, DVC data and remotes, DVC stages and plots, and more.

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

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 iterative 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.