Unlike traditional experiment tracking tools that use an external SaaS tool for collecting metrics, extension works in source-code editor VS Code and makes ML experiments easily reproducible

Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today announced a free extension to Visual Studio Code (VS Code), a source-code editor made by Microsoft, for experiment tracking and machine learning model development.

“Iterative’s experiment versioning technology that was implemented in DVC last year makes this reproducibility possible.”

VS Code is a coding editor that helps users to start coding quickly in any programming language. The DVC Extension for Visual Studio Code allows users of all technical backgrounds to create, compare, visualize, and reproduce machine learning experiments. Through Git and Iterative’s DVC, the extension makes experiments easily reproducible, unlike traditional experiment tracking tools that just stream metrics.

“This is an open source VS Code extension for machine learning practitioners looking to accelerate their model development experience,” said Ivan Shcheklein, co-founder and CTO of Iterative. “It simplifies data scientists’ machine learning model development workflows and meets ML modelers where they work. This extension eliminates the need for costly SaaS solutions for experiment tracking, turning VS Code into a native ML experimentation tool, built for developers.”

The extension complements the existing VS Code UX with features using the Command Palette, Source Control view, File Tree explorer, and even custom in-editor webviews, to aid data scientists in their model development and experimentation workflows. Users can pull and push versioned data, run and reproduce experiments, and view tables and metrics.

“Beyond the tracking of ML models, metrics, and hyperparameters, this extension also makes ML experiments reproducible by tracking source code and data changes,” said Dmitry Petrov, CEO of Iterative. “Iterative’s experiment versioning technology that was implemented in DVC last year makes this reproducibility possible.”

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The VS Code extension offers data scientists the ability to view, run, and instantly reproduce experiments with parameters, metrics, and plots all in a single place, as well as manage and version data sets and models. The extension also provides resource tracking so that data scientists can see which data sets and models have changed and allows exploration of all files of a project or model. Other features include live tracking to see how metrics change in real-time, cloud-agnostic data versioning and management, and native plot visualization.

The VS Code extension helps organizations:

• Reproduce every step of the data science model development process by versioning code, model meta-information, and experiments in one place
• Standardize their ML development environment with VS Code, eliminating the need for disparate tools across their data science teams
• Ease data scientist onboarding by providing an already-familiar IDE for faster model development and experiment tracking

DVC, the underlying open-source technology behind the extension, brings agility, reproducibility, and collaboration into the existing data science workflow. It provides users with a Git-like interface for versioning data and models, bringing version control to machine learning and solving the challenges of reproducibility. DVC is built on top of Git and creates lightweight metafiles, which enable the data science and ML teams to efficiently handle large files that otherwise can’t be stored.

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