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HomeNewsIterative announces machine learning-based extension for Microsoft Visual Studio Code

Iterative announces machine learning-based extension for Microsoft Visual Studio Code

The extension will simplify machine-learning model development workflows for data scientists and meet the ML modelers working there.

Iterative, the MLOps company working toward strategizing the workflow of data scientists and machine learning (ML) engineers, has announced a free extension for Visual Studio Code (VS Code). The extension is a source-code editor developed by Microsoft for machine learning model development and experiment tracking. 

The extension will simplify machine-learning model development workflows for data scientists and meet the ML modelers working there. The extension eliminates the need for expensive SaaS solutions for experiment tracking by turning VS Code into a native machine learning (ML) experimentation tool built for developers. 

VS Code is a coding editor that allows users to initiate coding in any programming language quickly. The DVC extension for Visual Studio Code will allow users from all technical backgrounds to create, visualize, compare, and reproduce machine learning experiments. The extension makes experiments easily reproducible through Git and Iterative’s DVC. This is unlike traditional experiment tracking tools that only stream metrics.

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The extension enhances the existing VS Code UX with features using Source Control view, Command Palette, File Tree explorer, and custom in-editor web views. These features aid data scientists in model development and experimentation workflows. Through this extension, users can run and reproduce experiments, pull and push versioned data, and view metrics and tables.

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

Additionally, the extension provides resource tracking for data scientists to see which data sets and models have changed. It also allows the exploration of all project or model files. Other features of the extension include live tracking of metrics, native plot visualization, and 

cloud-agnostic data versioning and management. 

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Sahil Pawar
Sahil Pawar
I am a graduate with a bachelor's degree in statistics, mathematics, and physics. I have been working as a content writer for almost 3 years and have written for a plethora of domains. Besides, I have a vested interest in fashion and music.

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