Use CML from a Bitbucket pipeline to provision an AWS EC2 instance and (re)train a machine learning model.

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.

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.

Why we believe extending best practices of software engineering to machine learning projects will streamline ML and AI development and keep all of us off the road to hell.

🚀 We are excited to release DVC Studio, the online UI for DVC and CML. Use DVC Studio for ML versioning, visualization, teamwork and no-code automation on top of DVC and Git. Read all about the exciting features and watch videos to get started quickly.

Today is DVC 2.0 release day! Watch a video from DVC-team when we explain the new features and read more details in this blog post.

Use your own GPUs with GitHub Actions & GitLab for continuous machine learning.