What is the difference between 
ModelOps and MLOps?

“Model operationalization (ModelOps) is primarily focused on the end-to-end governance and life cycle management of all analytics, AI and decision models …”
Gartner, Market Guide for AI Trust, Risk and Security Management,  September 2021
Avivah Litan, Farhan Choudhary, Jeremy D’Hoinne
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Report:
State of ModelOps
and MLOps
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Gartner Report: Market Guide for AI Trust, Risk and Security Management
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Video:
ModelOps vs MLOps

Difference Between ModelOps and MLOps
Resources
Webinars
2021 ModelOps and MLOps Summit
Keynote: “The AI Engineering Journey”
Erick Brethenoux
VP Analyst and AI Research Agenda Lead, Gartner
Panel Discussions featuring executives from Ally Bank, Charles Schwab, New York Life, Regions, KPMG, Cantor Fitzgerald, and more.
Gartner Webinar
Use ModelOps for an Effective AI Strategy
A panel of Gartner analysts share how ModelOps and MLOps makes enterprise AI initiatives more efficient and effective through governance, automation and orchestration.
Operationalize AI Initiatives
Stu Bailey, ModelOp Chief Enterprise AI Architect and Co-Founder shares the role of ModelOps (and MLOps) as the core of AI Orchestration platforms for Enterprise AI
eBooks
Survey Report
State of ModelOps 2021
The MLOps and ModelOps Survey Report summarizes the first ever research into AI operationalization, based on a survey of 100 executives from Fortune 500 companies.
4 Steps to Successful Model Operations
Learn the steps that any organization can take to successfully operationalize AI/ML or any other type of model.
ModelOps and MLOps explained
This whitepaper explains what ModelOps is, and how it relates with MLOps, as a response to the growing need of clarity from organizations that are scaling, automating and governing Enterprise AI initiatives.
RFP
ModelOps & MLOps RFP Template
This document is an example RFP for addressing ModelOps (and MLOps) functional requirements. It is the result of interviews with several industry experts and analysts.
Executive Panels
CXO Panel
Governing, Integrating and Implementing Model, Data, AI & ML Initiatives
Shrikant Dash, Financial Services and Analytics Executive, leads a panel discussion with leading industry executives:
  • Ron Bodkin, VP AI engineering and CIO, Vector Institute,
  • Detin Karakus, Global head of quantitative and analytical soltuions, BP
  • Jacob Kosoff, Head of model risk, Regions Bank
  • Agus Sudjianto, EVP and head of coporate model risk, Wells Fargo
ModelOps Executive Panel Series #2
Bring Enterprise AI Initiatives into production with ModelOps
  • Leading Analyst view on the ModelOps and MLOps space
  • Executive practitioners’ sharing their experiences on how to operationalize all models across the enterprise in a world with models of all types, regulations and the need for explainability and visibility.
  • Lessons learned, what has worked, what hasn’t, and how ModelOps (including MLOps) is a key capability for enterprise AI to adapt and thrive in the new normal we are in.
Master Classes
ModelOps Master Class
Best Practices to Deploy, Monitor & Govern AI/ML Models

Stu Bailey, ModelOp co-founder and Chief AI Architect, covers the basics and best practices of ModelOps and MLOps and how customers can scale and govern their models

ModelOps Technical Master Class
Become an Enterprise AI Architect
A series of 3 videos. Learn how to design model life cycles in real-life scenarios, including business KPIs and bias monitoring.
ModelOps Technical Master Class
ModelOps and MLOps Technical Deep Dive
A series of 5 videos. Learn how to deploy, monitor and govern AI and ML models. Take a deep dive into MLOps, which is a subset of ModelOps that is focused on managing machine learning models.
Connect with a ModelOps expert
Gartner Disclaimer
Any reference made to “ModelOps” is about model operations platforms, not the company ModelOp. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.