# Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution

DevFeed: [Introducing Glassdoor's ML Registry: A Centralized Artifact Management Solution](<https://devfeed.tech/articles/introducing-glassdoor-s-ml-registry-a-centralized-artifact-management-solution-22612.md>)

Original publisher: [Read original article](<https://medium.com/glassdoor-engineering/introducing-glassdoors-ml-registry-a-centralized-artifact-management-solution-8bff3151cd9d?source=rss----288d984af747---4>)

Author: Rich Papalia

Published: 2023-08-30T21:20:29Z

Content type: article

Language: en

Sources: [Glassdoor Engineering](<https://devfeed.tech/sources/glassdoor-engineering.md>)

Topics: [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Development](<https://devfeed.tech/topics/development.md>), [Amazon SageMaker AI](<https://devfeed.tech/topics/amazon-sagemaker-ai.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [configuration-management](<https://devfeed.tech/tags/configuration-management.md>), [feature-store](<https://devfeed.tech/tags/feature-store.md>), [lifecycle](<https://devfeed.tech/tags/lifecycle.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [machine-learning-platform](<https://devfeed.tech/tags/machine-learning-platform.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [registry](<https://devfeed.tech/tags/registry.md>)

## AI overview

Glassdoor describes its newly open-sourced ML Registry, a centralized service for managing machine-learning artifacts and related metadata. The registry is intended to provide a single source of truth and consistent access across teams and applications, integrating with other tools and services.

## Source excerpt

Photo by Ryunosuke Kikuno on UnsplashGlassdoor's Machine Learning Engineering and Platform Team As part of Glassdoor's journey to becoming an ML-driven company, we've created an exceptional Machine Learning Engineering and Platform team, (see our blog about building this team here) dedicated to developing the foundational infrastructure for all of our ML initiatives. We've strategically built our ML platform by utilizing a combination of buying, building, and adopting from existing open-source solutions. This approach has allowed us to leverage the best of all worlds. Our team has had the opportunity to build numerous tools from scratch, including data pipelines, human-in-the-loop tools, and more. We've also built on top of existing tools like AWS Sagemaker to incorporate powerful in-house customizations into our feature store. We have more exciting plans in flight, such as building a new recommendations platform, further open-source contributions from our platform, and continuing to build on the foundation which we've established. Today, we'll delve into our newly open-sourced ML Registry. What is an ML Registry? The ML lifecycle encompasses far more than just model development. Once a model is built, many questions arise: Where does it reside? How do we access it? What if updates or versioning are needed? Where can we store the metadata describing the model? And what about other non-model artifacts? How can we efficiently manage all of this? Enter the ML Registry. Glassdoor's ML Registry is a centralized management service for ML artifacts and all related metadata. It serves as the single source of truth for all data pertaining to ML, enabling uniform and reliable access to this data across diverse teams and applications. It seamlessly integrates with other tools and services and provides robust, feature-rich functionality. Choosing Between Buying and Building, What Differentiates Our ML Registry? While several paid and open-source model registries already exist,