“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
Gartner Report: Market Guide for AI Trust, Risk and Security Management
MLOps is for Data Scientists
ModelOps is for the Enterprise
Top 10 Questions about MLOps and ModelOps
What is MLOps?
What is ModelOps?
What types of capabilities are common in MLOps tools?
What type of models are handled by MLOps tools?
What types of capabilities are common in ModelOps solutions?
Is there overlap between MLOps and ModelOps?
Does ModelOps diminish the investment that has already been made in MLOps tools?
What is ModelOp Center?
What makes ModelOp Center different from other ModelOps solutions?
What if I have tools that overlap with the capabilities that ModelOp Center provides? Will I lose the investment I have made in those tools and solutions?
MLOps and ModelOps: What’s the Difference and Why It Matters
These two terms are often used interchangeably. However, there are key distinctions between the functionality and features each provide, and the AI value and scalability at your organization depend on them.
Don’t Let Tooling and Management Approaches Stifle Your AI Innovation
The possibilities for AI use grow almost daily, so it’s important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions.
ModelOps vs MLOps: What’s the Difference and Why Should You Care
Jim Olsen, CTO at ModelOp, discusses and shows how ModelOps, the discipline of managing all types of models and MLOps, managing only machine learning models, are different in model operational requirements.
