How Are Model Life-Cycle Aspects of Enterprise Intelligence Operationalized Through MLOps to Deliver Insights at Scale?

How Are Model Life-Cycle Aspects of Enterprise Intelligence Operationalized Through MLOps to Deliver Insights at Scale?

This IDC Perspective provides an overview of ModelOps, key components of delivering insights at scale — one of the pillars of enterprise intelligence. Given the similarity between the life cycles of both ML and non-ML models, this IDC Perspective provides best practices leveraging MLOps capabilities to operationalize model life-cycle management for both ML and non-ML models."Key quantitative benefits from enterprise intelligence investments include accelerated time to market with new offerings, increased employee retention, and revenue growth," says Sriram Subramanian, research director, AI and Automation Software research at IDC. "Model operations are key aspects of delivering insights at scale. With the proliferation of diverse models, firmly established MLOps practices can enable organizations to leverage common tools and processes to manage both ML and non-ML models."

Please Note: Extended description available upon request.


Executive Snapshot
Situation Overview
Pillars of Enterprise Intelligence
Model Life-Cycle Management
How Can MLOps Help?
Advice for the Technology Buyer
Extend MLOps to Beyond ML Models
Establish Synergy Between Ops Practices
Leverage ML to Expand the Capacity to Learn
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Related Research
Synopsis

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