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Iterative Blogs
Find here Iterative news, findings, interesting reads, community takeaways, deep dive into machine learning workflows from data versioning and processing to model productionization.
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.
Training and saving models with CML on a dedicated AWS EC2 runner (part 2)
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.
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.
April '22 Community Gems
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.
Machine Learning Workloads with Terraform Provider Iterative
Today we introduce painless resource orchestration for your machine learning projects in conjunction with HashiCorp Terraform.
Training and saving models with CML on a self-hosted AWS EC2 runner (part 1)
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.
April '22 Heartbeat
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!
Preventing Stale Models in Production
We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.
