Managing Model Registries

Managing model registries is a crucial aspect of machine learning operations (MLOps) that involves the systematic organization and tracking of machine learning models throughout their lifecycle. A model registry serves as a central repository where models can be stored, versioned, and documented, facilitating collaboration among teams. It allows for the management of model states, such as testing, production, or archived, and supports the promotion and approval of model versions. Effective model registry management enhances model quality, ensures compliance, and streamlines the deployment process, ultimately leading to more efficient and reliable machine learning workflows.

Register and Deploy Models with SageMaker Model Registry

Towards Data Science

An Introduction To SageMaker Model Registry Continue reading on Towards Data Science

Model Monitoring Dashboards made easy (1/3).

Towards AI

I know the pain of model monitoring and retraining models is not very pleasant for any machine learning engineer, more so if there isn’t any easy way to keep track of all the models that are deployed…...

ML model registry — the “interface” that binds model experiments and model deployment

Towards Data Science

ML model registry — the “interface” that binds model experiments and model deployment. MLOps in Practice — A deep- dive into ML model registries, model versioning and model lifecycle management..

Advent of 2022, Day 14 – Registering the models

R-bloggers

In the series of Azure Machine Learning posts: Important asset is the “Models” in navigation bar. This feature allows you to work with different model types -__ custom, MLflow, and Triton. What you do...

How To Effectively Manage Deployed Models

Towards Data Science

Most models never make it to production. We previously looked at deploying Tensorflow models using Tensorflow Serving. Once that process is completed, we may think that our work is all done. In…

MLOps in a Nutshell: Model Registry, ML Metadata Store and Model Pipeline

Python in Plain English

The following is a collection of three shorter-form content pieces I’ve published on LinkedIn. They present three core MLOps (Machine Learning Operations) concepts in a concise manner: * Model Registr...

Models and databases

Django documentation

A model is the single, definitive source of information about your data. It contains the essential fields and behaviors of the data you’re storing. Generally, each model maps to a single database tabl...

Custom Model Managers in Django

Python in Plain English

Custom Model Managers In Django A manager is an interface through which database query operations are provided to Django models. At least one Manager exists for every model in a Django application, ob...

Improving Model Management Practices for Machine Learning Teams

Towards Data Science

In this article, we shed a light on how to identify issues in making Machine Learning (ML) activities more efficient primarily through model management capabilities. However, we do not propose any…

Securing GenAI: Vol 5 — Model deployment and change management

Towards AI

Securing GenAI: Vol 5 — Model Deployment and Change Management Written by Manu Chatterjee, Head of AI at Leapfrog Technology Deploying AI models securely requires strict version control, monitoring, ...

Automate your Model Documentation using H2O AutoDoc

Towards Data Science

The Federal Reserve’s 2011 guidelines state that model risk assessment and management would be ineffective without adequate documentation. A similar requirement is put forward today by many…

Models

Django documentation

Model API reference. For introductory material, see Models . Model field reference Field attribute reference Model index reference Constraints reference Model _meta API Related objects reference Model...