How Are Model Life-Cycle Aspects of Enterprise Intelligence Operationalized Through MLOps to Deliver Insights at Scale?
Description
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.
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.
Table of Contents
7 Pages
- 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
- Learn More
- Related Research
- Synopsis
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