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.
Building Trust in Your AI
Amazon Web Services (AWS)
AWS is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from datacenters globally. Millions of customers are using AWS to lower costs, become more agile, and innovate faster.
CloudFactory is a global leader in combining people and technology to provide a workforce in the cloud for machine learning and core business data processing.
Cognilytica is an AI focused research, advisory, and education firm.
Databricks is the data and AI company. Thousands of organizations worldwide rely on Databricks’ open and unified platform for data engineering, machine learning and analytics. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to solve the world’s toughest problems.
Maverick Quantum Inc (mavQ)
Maverick Quantum Inc (mavQ) is a low code & artificial intelligence platform that enables organizations with digital transformation while creating valuable insights and outcomes.