A guide to understanding and managing AI performance, operationalization, and impact
Learn how to ensure
high-performing, trustworthy AI
In order for organizations to capture the potential value of AI, they require the frameworks, tools, and processes to evaluate, improve, and monitor AI Quality. This whitepaper covers how data science and ML engineering teams can drive better AI Quality, and why AI Quality is a consideration that should be built at the outset into any AI development initiative.
Read this whitepaper and you will learn:
- The quality and performance challenges of ML - and how they're connected
- The four pillars of AI Quality
- Key processes and tools for driving AI Quality
- Why a lifecycle approach to AI Quality is best
- Why AI Quality management needs to be a consideration from Day 1
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Read the whitepaper
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.