Many AI projects fall short of expectations due to poor model performance or the unintended consequences of inaccurate AI decisions. What if there was a universal way for MLOps/AIOps to evaluate and monitor the performance and behavior of AI models, both pre-deployment and ongoing, no matter the vendor or features used? In this session, we will review the pitfalls of opaque AI models, and discover how to evaluate, compare, and monitor performance and behavior across AI models, for better AI model trust and explainability.
Featured Presenters
Event Sponors
Amazon Web Services (AWS)
Government, education, and nonprofit organizations face unique challenges to accomplish complex missions with limited resources. Tens of thousands of public sector organizations around the world
CloudFactory
CloudFactory combines people and technology to provide cloud workforce solutions. Our professionally managed and trained teams can process data for machine learning with high accuracy
Cognilytica
Cognilytica is an AI, ML, and data focused advisory and education firm. Cognilytica serves Public Sector and industry clients with AI, ML, and Cognitive technology
Databricks
Databricks is the data and AI company. Thousands of organizations worldwide — including Comcast, Condé Nast, Nationwide and H&M — rely on Databricks’ open and
Maverick Quantum Inc (mavQ)
mavQ provides an AI driven Low Code platform that allows enterprises to imagine, innovate, and transform their digital journey. Convert your ideas into production scale
