MLOps Company Iterative Introduces First Open Machine Learning Model Deployment and Management Tool Based on Git | New


Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning (ML) engineers, today launched Machine Learning Engineering Management (MLEM) – an open model registry and deployment tool source that uses an organization’s existing Git infrastructure and workflows.

MLEM bridges the gap between ML engineers and DevOps teams. DevOps teams can easily understand the underlying frameworks and libraries a template uses and automate deployment in a one-step process for production services and applications.

“Iterative allows customers to treat AI models like another type of software artifact,” said Sriram Subramanian, research director, AI/ML Lifecycle Management Software, IDC. “The ability to create ML model registries using the Git framework and DevOps principles allows models to go into production faster.”

MLEM is a building block for a Git-based ML model registry, as well as other iterative tools, such as GTO and DVC. A model registry stores and versions the trained ML models. Model registries greatly simplify the task of tracking models throughout their ML lifecycle, from training to production deployments and ultimately retirement.

“Model registries simplify tracking models as they move through the ML lifecycle by storing and managing versions of trained models, but organizations that create these registries end up with two different technology stacks for model registries. learning and software development,” said Dmitry Petrov, co-founder and CEO of Iterative. . “MLEM as the building block for model registries uses Git and traditional CI/CD tools, aligning ML and software teams so they can get models into production faster.”

With iterative tools, organizations can create a registry of ML models based on software development tools and best practices. This means that Git acts as a central source of truth for models, eliminating the need for machine learning-specific external tools. All of the information around a model, including what’s in production, development, or deprecated, can all be viewed in Git.

The modular nature of MLEM fits into any organization’s Git and CI/CD-based software development workflows, without engineers having to switch to a separate machine learning deployment and registry tool. This allows teams to use a similar process for ML models and deployment applications, eliminating process and code duplication. Teams can then create a model register in hours rather than days.

MLEM promotes a complete machine learning model lifecycle management workflow using a GitOps-based approach. Software development and MLOps teams can then be aligned, using the same tools to accelerate the time it takes for a model to move from development to production.

Iterative was founded in 2018 and in less than three years its tools have had over 8 million sessions and are growing rapidly, with over 12,000 stars on GitHub between CML and DVC. DVC users grew by almost 95% in 2021 with over 3000 monthly users. Iterative now has more than 300 contributors to the various tools.

Visit the website and read the blog to learn more about MLEM.

About iterative, the company behind Iterative Studio and popular open source tools DVC, CML and MLEM, enables data science teams to build models faster and collaborate better with machine learning tools data-centric. 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

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SOURCE: Iterative

Copyright BusinessWire 2022.

PUBLISHED: 06/01/2022 09:00/DISC: 06/01/2022 09:03

Copyright BusinessWire 2022.

Sherry J. Basler