Gen AI: Enterprise-Grade Model Maintenance – MLOps for Scalable, Secure AI and Gen AI solutions | Kisaco Research
Speaker(s): 

Author:

Ahmed Menshawy

Vice President of AI Engineering
Mastercard

Ahmed Menshawy is the Vice President of AI Engineering at Mastercard. In this role, he leads the AI Engineering team, driving the development and commercialization of AI products that deliver business value across the organization. His work focuses on enabling Mastercard to harness the power of AI to solve complex problems, optimize operations, and enhance customer experiences. Ahmed collaborates with cross-functional teams to bring innovative AI solutions from concept to market, ensuring they align with Mastercard’s strategic objectives

An accomplished author and recognized contributor to the AI community, Ahmed has co-authored Deep Learning with TensorFlow and written Deep Learning by Example. His most recent book, Graph Learning for the Enterprise, published by O’Reilly, provides practical insights into efficiently training and deploying graph learning pipelines at scale

 

Ahmed Menshawy

Vice President of AI Engineering
Mastercard

Ahmed Menshawy is the Vice President of AI Engineering at Mastercard. In this role, he leads the AI Engineering team, driving the development and commercialization of AI products that deliver business value across the organization. His work focuses on enabling Mastercard to harness the power of AI to solve complex problems, optimize operations, and enhance customer experiences. Ahmed collaborates with cross-functional teams to bring innovative AI solutions from concept to market, ensuring they align with Mastercard’s strategic objectives

An accomplished author and recognized contributor to the AI community, Ahmed has co-authored Deep Learning with TensorFlow and written Deep Learning by Example. His most recent book, Graph Learning for the Enterprise, published by O’Reilly, provides practical insights into efficiently training and deploying graph learning pipelines at scale

 

Time: 
5:30 PM
Agenda Track No.: 
Track 2
Session Type: 
Track
Session Stage: