
AI Observability: New Tools and Processes Emerge for Operating and Maintaining AI/ML Workloads in the Enterprise
Description
AI Observability: New Tools and Processes Emerge for Operating and Maintaining AI/ML Workloads in the Enterprise
This IDC Perspective examines the emerging AI observability subsegment, offering advice and insight to enterprises that are investigating how best to monitor and manage production ML and AI applications. "Many enterprises are just now turning a corner. They've developed and launched AI- or ML-driven capabilities, such as recommendation engines, chatbots, or pricing engines, and now realize those capabilities must be monitored, managed, and maintained," said Nancy Gohring, research director, Enterprise System Management, Observability, and AIOps Software at IDC. "A number of start-ups have emerged in a new subcategory, AI observability, aiming to provide organizations with the tools they need to support accurate and reliable AI- and ML-driven services and applications."
Please Note: Extended description available upon request.
Table of Contents
6 Pages
- Executive Snapshot
- Situation Overview
- WhyLabs
- Arize
- Mona Labs
- Others
- Established Observability Vendors
- Advice for the Technology Buyer
- Learn More
- Related Research
- Synopsis
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