Learn how a leading energy company is using AI to drive its generation business metrics, and full lifecycle AI observability to optimize its AI operations

AI Observability is the most important part of an ML program where you probably still have gaps. Without full lifecycle AI observability, your ML program risks missing opportunities to improve models and succumbing to common AI failure scenarios that minimize lasting change and adoption. Common failure scenarios range from model failures, KPI performance failures, and data quality issues, to operational and societal factors, all of which impact the value your business teams get from using those models. But how do you get the visibility you need to successfully develop, test and monitor your AI apps?

Join us on September 28th as Sean Otto, Director of Analytics at the AES Corporation joins TruEra’s President and Chief Scientist, Anupam Datta, to give you both an overview and real-life examples of the latest in AI observability. During the 45-minute webinar, you will learn:

• Common challenges to maintaining AI adoption at AES to maximize profitability and sustainability
• The benefits of full lifecycle AI Observability
• The top 5 AI application failure scenarios and how to avoid them
• How AES is using AI observability to better partner with the business organization while improving models and answer modeling questions
• New cutting edge techniques to better monitor and debug production models
• Emerging best practices in LLM application observability

Simply fill out the form and click "Submit" to watch now. 

Meet the Speakers