This is a Panel taken from the recent AI Quality Conference presented by the MLOps COmmunity and Kolena

// Abstract Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks, and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes. This session, featuring veteran practitioners, focuses on building scalable, reliable, and quality AI and ML systems for the enterprises. // Panelists - Hira Dangol: VP, AI/ML and Automation @ Bank of America - Rama Akkiraju: VP, Enterprise AI/ML @ NVIDIA - Nitin Aggarwal: Head of AI Services @ Google - Steven Eliuk: VP, AI and Governance @ IBM A big thank you to our Premium Sponsors Google Cloud & Databricks for their generous support!

Timestamps:

00:00 Panelists discuss vision and strategy in AI

05:18 Steven Eliuk, IBM expertise in data services

07:30 AI as means to improve business metrics

11:10 Key metrics in production systems: efficiency and revenue

13:50 Consistency in data standards aids data integration

17:47 Generative AI presents new data classification risks

22:47 Evaluating implications, monitoring, and validating use cases

26:41 Evaluating natural language answers for efficient production

29:10 Monitoring AI models for performance and ethics

31:14 AI metrics and user responsibility for future models

34:56 Access to data is improving, promising progress

A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON 35:38 Share

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Shownotes Transcript

A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON