SAN FRANCISCO–(BUSINESS WIRE)–IterativeMLOps, a company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today announced a free extension for Visual Studio Code (VS Code), a source code editor created by Microsoft, for tracking experiments and developing machine learning models.
VS Code is a coding editor that helps users start coding quickly in any programming language. The DVC Extension for Visual Studio Code empowers users of all technical backgrounds to create, compare, visualize, and replicate machine learning experiments. Via Git and Iterative CVDthe extension makes experiments easily repeatable, unlike traditional experiment tracking tools that just stream metrics.
“This is an open source VS Code extension for machine learning practitioners looking to speed up their model development experience,” said Ivan Shcheklein, co-founder and CTO of Iterative. “It simplifies machine learning model development workflows for data scientists and meets ML modelers where they work. This extension eliminates the need for expensive SaaS solutions for tracking experiments, turning VS Code into a native ML experimentation tool, designed for developers.
The extension complements the existing VS Code UX with functionality using the command palette, source control view, file tree explorer, and even custom web views in the editor, to help data scientists data in their model development and experimentation workflows. Users can extract and transmit versioned data, run and reproduce experiments, and view tables and metrics.
“Beyond tracking ML models, metrics, and hyperparameters, this extension also makes ML experiments repeatable by tracking source code and data changes,” said Dmitry Petrov, CEO of Iterative. “Iterative test version control The technology that was implemented in DVC last year makes this reproducibility possible.”
The VS Code extension gives data scientists the ability to instantly view, run, and replicate experiments with parameters, metrics, and plots all in one place, as well as manage and version datasets and models. The extension also provides resource tracking so data scientists can see which datasets and models have changed and allows exploration of all files in a project or model. Other features include live tracking to see how metrics are changing in real time, cloud-independent data versioning and management, and native plot visualization.
The VS Code extension helps organizations to:
Replicate every step of the data science model development process by managing code versions, 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
Make it easier for data scientists to onboard by providing an already familiar IDE for faster model development and experiment tracking
DVC, the open source technology underlying 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 reproducibility challenges. DVC is built on top of Git and creates lightweight metafiles, which allow data science and ML teams to efficiently manage large files that otherwise could not be stored.
Iterative.aithe company behind Iterative Studio and popular open source tools CVD, CMLand MLEM, enables data science teams to build models faster and collaborate better with data-centric machine learning tools. Iterative’s developer-focused approach to MLOps provides model reproducibility, governance, and automation throughout the ML lifecycle, all tightly integrated into software development workflows. Iterative is a remote company, backed by True Ventures, Afore Capital and 468 Capital. For more information, visit Iterative.ai.
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