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VMblog Expert Interview: Dmitry Petrov Explores Iterative Studio Model Registry


Iterative, an open platform to operationalize AI, has launched its Iterative Studio Model Registry.  To find out more about its benefits and the problems that it solves, VMblog spoke with Dmitry Petrov, the company's co-founder and CEO.

VMblog:  Before we begin diving into your most recent announcement, tell our readers about who Iterative is and what the company does?

Dmitry Petrov:  Sure. Founded in 2018, Iterative is an MLOps company dedicated to streamlining the workflow of data scientists and Machine Learning (ML) engineers. We're the company behind popular open-source tools like DVC, CML, MLEM, and DVC Extension for VS Code, which enable data science teams to build models faster and collaborate better with data-centric machine learning tools. Today, Iterative tools have had more than 8 million sessions earning more than 14,000 stars on GitHub and have more than 300 contributors across their different tools.

VMblog:  Congratulations on the launch of Iterative Studio Model Registry. Can you explain to our readers what this is? 

Petrov:  Iterative Studio Model Registry is a model registry solution so teams can easily manage models with full context around model lineage, version, production status, data used to train model, and more. The Iterative Studio Model Registry uses a GitOps approach for model lifecycle management, meaning an organization's Git is the single source of truth.

VMblog:  What problem does Iterative Studio Model Registry solve?

Petrov:  Based on engagement with hundreds of organizations across industries of various sizes, Iterative found that more than 80% of organizations do not have the necessary visibility and control over their ML models or how they're deployed throughout the ML model development lifecycle. Unlike existing solutions that are separate from software development tools and often not updated with the latest model information, Iterative takes the workflows and best practices of software development and applies them to model deployment, getting models into production faster.

VMblog:  It is my understanding that Iterative Studio Model Registry is made with full modular components. What is the benefit with that? 

Petrov:  It's about meeting the user where they are at. Whether it's a data scientist who prefers APIs, a manager who prefers a web user interface, or a DevOps engineer who works best with the command line interface (CLI), Iterative Studio Model Registry provides all the options. This way, team members use the interface that they're most comfortable with in order to create and collaborate on ML models quickly and seamlessly. Organizations can also pick-and-choose what tools they use from Iterative based on what they already have in their MLOps stack.

VMblog:  Iterative Studio Model Registry allows for sophisticated management of models across the ML lifecycle, from development to production and retirement. What are the benefits here? 

Petrov:  There are three primary benefits to these capabilities:

  • Model organization, access, and collaboration: Explore models in a central dashboard that facilitates model discovery across all your ML projects. Model history, versions, and stages are transparent and accessible across the team.
  • Model versioning and lineage: Register and track models and their versions from a GUI. Identify the experiment that produced the model and track how, when and by whom a model version was created. For highly-regulated industries like health or finance, a single place for all information regarding models that teams can easily search and access is an indispensable requirement.
  • Model lifecycle management: Manage the lifecycle of each model as it moves through staging, production, and other stages. See at a glance which model versions are in which stage and move easily across stages within the interface.
Published Tuesday, July 26, 2022 8:01 AM by David Marshall
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