Iterative, an MLOps platform that raised $20 million in a Series A round almost exactly a year ago, has launched MLEM, an open source Git-based machine learning model management and deployment tool.
The goal here, according to the company, is to bridge the gap between ML engineers and DevOps teams by utilising the git-based approach that developers are already familiar with. Developers can use MLEM to store and track their machine learning models throughout their lifecycle. As such, it complements Iterative’s open source GTO artefact registry and DVC, the company’s data and model version control system.
According to the team, a system like this allows for easier model sharing between business units and teams, as well as making it easier for ML teams to collaborate with their DevOps teams. A system like this also provides a single source of truth for determining the lineage of a given model in highly regulated industries.
Iterative, of course, provides a hosted platform that does all of these things via its Iterarative Studio service for collaborating on ML models, tracking experiments and visualisations, and hosting a model registry.
“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 Petrov. “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 faster.”
“Having a machine learning model registry is becoming an essential part of the machine learning technology stack. Current SaaS solutions can lead to a divergence in the lifecycle of ML models and software applications,” said Dmitry Petrov, co-founder and CEO of Iterative.
