Iterative Machine Learning Engineering Management GA
June 6, 2022
Launched Machine Learning Engineering Management (MLEM) – an open source model
deployment and registry tool 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 model uses and
automate deployment into a one-step process for production services and apps.
“Iterative enables customers to treat AI models as just another type of software
artifact,” said Sriram Subramanian, research director, AI/ ML Lifecycle
Management Software, IDC. “The ability to build ML model registries using Git
infrastructure and DevOps principles allows models to get into production
MLEM is a core building block for a Git-based ML model registry, together with
other Iterative tools, like GTO and DVC. A model registry stores and versions
trained ML models. Model registries greatly simplify the task of tracking models
as they move through the ML lifecycle, from training to production deployments
and ultimately retirement.
“Model registries simplify tracking models moving through the ML lifecycle by
storing and versioning trained models, but organizations building these
registries end up with two different tech stacks for machine learning models and
software development,” said Dmitry Petrov, co-founder and CEO of Iterative.
“MLEM as a building block for model registries uses Git and traditional CI/CD
tools, aligning ML and software teams so they can get models into production
With Iterative tools, organizations can build a ML model registry based on
software development tools and best practices. This means Git acts as a central
source of truth for models, eliminating the need for external tools specific to
machine learning. All information around a model including which are in
production, development, or deprecated, can all be viewed in Git.
modular nature fits into any organization’s software development workflows based
on Git and CI/CD, without engineers having to transition to a separate machine
learning deployment and registry tool. This allows teams to use a similar
process across both ML models and applications for deployment, eliminating
duplication in processes and code. Teams are then able build a model registry in
hours rather than days.
MLEM promotes a comprehensive 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 speed the time it takes a model to get
from development to production.
Iterative was founded in 2018 and in less than three years, its tools have had
more than 8 million sessions and are rapidly growing, with more than 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 across the