ModelOps RFP Template

The Pillars of ModelOps are:
Executive Visibility
Your window into AI across the enterprise
Do you want to know the answers to:
  • What is the ROI for my AI initiatives?
  • What models are in production?
  • Are models performing within business and risk thresholds?
Model Risk Industrialization
Multiple your model validation workforce by 30% or more with automation
Do you want to know the answers to:
  • How can I shorten the time for model validation?
  • What were the results of the validated models?
  • Are models performing within risk controls?
AI Orchestration
Manage and monitor all models in production and deliver them 50% faster
Do you want to know the answers to:
  • How long does it take to get models into production?
  • Are there out-of-the-box integrations with data science tools and existing systems?
  • Are models performing within established thresholds?
These documents are example RFPs for addressing the functional requirements of ModelOps. These have been created as a result of interviews with several industry experts and analysts. ModelOps is a key capability that is required for successful AI/ML model operations across the enterprise once models have been developed.

Industry experts and analysts now recognize that model development and model operations are different disciplines, requiring different capabilities, tools and even teams.

Gartner defines ModelOps as:
Artificial intelligence (AI) model operationalization (ModelOps) is a set of capabilities that primarily focuses on the governance and the full life cycle management of all AI and decision models. This includes models based on machine learning (ML), knowledge graphs, rules, optimization, natural language techniques and agents. In contrast to MLOps (which focuses only on the operationalization of ML models) and AIOps (which is AI for IT operations), ModelOps focuses on operationalizing all AI and decision models.
Gartner Glossary