New AI challenges require new solutions
The same old analytical approaches don't work for AI. Find out why.
New AI applications are complex, hard to explain, difficult to measure, and constantly evolving
Managing the performance of new AI applications is critical to achieving critical business objectives. But the AI/ML systems that power these applications can fail in a number of different ways — failures ranging from poor model performance to a breakdown in wider organizational processes.
Systematically managing these failure scenarios requires a new set of technology capabilities and approaches. Today, ML teams try addressing these failure modes with old methods, like existing KPI or infrastructure monitoring software, or automated model retraining. However, none of these methods are fully effective.
This new whitepaper explains the key requirements and solutions for managing AI application quality, including:
- The top challenges in managing AI app performance
- The 3 methods for analyzing and improving AI performance, and their strengths and weaknesses
- The AI Observability gap and how to overcome it
- Why full lifecycle AI Observability is the fastest, easiest way to manage AI performance
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TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.