In today's rapidly evolving data landscape, establishing a robust governance framework for Data and AI is crucial. This session will constitute a vision for a comprehensive Data and AI Governance platform, detailing essential functionalities such as data lineage, policy enforcement, risk management, and compliance tracking. We will discuss key considerations for successful implementation, including stakeholder engagement, scalability, and integration with existing systems. Additionally, the session will highlight emerging trends in Generative AI that organizations should consider when designing their governance strategies. 

Key Takeaways:

• What is Data and AI governance and why it’s needed 
• Essential functionalities of a Data and AI Governance platform
• Implementation Strategies: Strategies for effective implementation, Federated vs Centralized, focusing on scalability, integration, and stakeholder engagement
• Emerging Gen AI Trends: current trends in Generative AI to enhance governance frameworks
• Industry best practices 

Speaker: Lakshmi Isukapally

Lakshmi is Vice President -Principal Architect, Data Architecture, at Americanexpress. Lakshmi is responsible for Data Architecture practice, for consistent data architecture across all critical data domains, Enterprise Data Management, tooling for automated data risk mitigation, for consistent data architecture across critical data domains. Her domain expertise includes Data Architecture and Data engineering Technologies (SQL & NoSQL, Vector DB databases, streaming platforms), Data Management, Distributed Systems Architecture and Design, Microservices Architecture, Event-Driven Design, Machine Learning Engineering, and Cloud Platforms

Speaker: Amol Salunkhe

Amol Salunkhe is the Principal Engineer at Americal Express, focusing on Enterprise AI/ML Services, Enterprise AI/ML, and Platform and Enterprise Personalization, and he leads the AI/ML CTP.  Amol joined Amex in Dec 2019 and has been leading several initiatives that cut across AI/ML Governance, Machine Learning Ops, Distributed Systems, and Platform Engineering. Prior to joining Amex, Amol's work focused on building Data Driven Decision Systems. Amol has PhD in Machine Learning from the University at Buffalo, and his research work on AI/ML systems and Algorithms has been published in several Internal Journals.