Iterative launches machine learning management tool

Iterative launches machine learning management tool


Iterative, the MLOps company dedicated to streamlining the workflow of data scientists and machine learning engineers (MLs), has launched Machine Learning Engineering (MLEM): a tool for deploying and recording open source models that use an organization’s existing Git infrastructure and workflows.

According to the company, MLEM is designed to bridge the gap between ML engineers and DevOps teams. DevOps teams can understand the underlying frameworks and libraries that a model uses and automate deployment in a one-step process for production services and applications, iterative claims.

IDC AI / ML Lifecycle Management Softwrae Research Director Sriram Subramanian says: “The iterative allows customers to treat AI models as another type of software artifact. The ability to create ML model records using the Git infrastructure and DevOps principles allows models to go into production faster.

MLEM is a basic block for Git-based ML model registration, along with other iterative tools such as GTO and DVC. A register of models stores and trained versions of ML models. Model records greatly simplify the task of tracking models as they move through the life cycle of ML, from training to production deployments, and finally retirement.

Co-founder and Iterative CEO Dmitry Petrov says: “Model registries simplify tracking models that move through the ML lifecycle by storing and versioning trained models, but the organizations that create these registries end up with two different technology stacks for to machine learning and software development models.

“MLEM as a core element for model logging uses Git and traditional CI / CD tools, aligning ML equipment and software so that they can introduce models into production more quickly.”

With iterative tools, organizations can create a ML model registry based on software development tools and best practices, the company says. This means that Git acts as a true central source for models, eliminating the need for specific external tools for machine learning.

All information around a model, including that in production, development, or obsolete, can be viewed in Git. The modular nature of MLEM adapts to any organization’s software development workflows based on Git and CI / CD, without the need for engineers to switch to an independent machine learning registration and deployment tool. This allows teams to use a similar process in both ML models and deployment applications, eliminating duplication of processes and code.

Teams can then create a record model in hours instead of days. MLEM promotes a comprehensive lifecycle management workflow of the machine learning model through a GitOps-based approach. Software development teams and MLOps can 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. Since then, 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 more than 3,000 monthly users. Iterative now has more than 300 collaborators through the different tools.



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