Enterprise end users | Kisaco Research

Why should Enterprise end users attend the Enterprise Generative AI Summit?

  • Hear from hyperscalers and cross-industry enterprises leading the way in generative AI deployments from across the finance, retail, pharma, tech, transport, communications, and other sectors.
  • Meet an eco-system of experts working on delivering safe and affordable generative AI for enterprise including model builders, hardware vendors, software vendors, generative AI application startups, consultancies and more.

Featured Speakers Include

 

Daniel Wu

Course Facilitator
Stanford University

Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.

Daniel Wu

Course Facilitator
Stanford University

Daniel Wu

Course Facilitator
Stanford University

Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.

Daniel is passionate about the democratization of technology and the ethical use of AI - a philosophy he shares in the computer science and AI/ML education programs he has contributed to over the years.

 

Hemant Jain

Senior Software Engineer, Inference
Cohere

Hemant works on efficiently fine-tuning and serving LLMs as a part of the Platform team at Cohere. Prior to this, he spent 3 years at NVIDIA developing Triton Inference Server, an open-source solution used to deploy machine learning models into production. He has a Masters in Data Science from the University of Washington.

Hemant Jain

Senior Software Engineer, Inference
Cohere

Hemant Jain

Senior Software Engineer, Inference
Cohere

Hemant works on efficiently fine-tuning and serving LLMs as a part of the Platform team at Cohere. Prior to this, he spent 3 years at NVIDIA developing Triton Inference Server, an open-source solution used to deploy machine learning models into production. He has a Masters in Data Science from the University of Washington.

 

Donna Schut

Head of Technical Solutions Management, Generative AI & Large Scale ML
Google Cloud

Donna Schut

Head of Technical Solutions Management, Generative AI & Large Scale ML
Google Cloud

Donna Schut

Head of Technical Solutions Management, Generative AI & Large Scale ML
Google Cloud
 

Melissa Harup

SVP and Chief Counsel
Mondelez International

Melissa Harup

SVP and Chief Counsel
Mondelez International

Melissa Harup

SVP and Chief Counsel
Mondelez International

Agenda highlights


OPENING KEYNOTE: The ethical implications of generative ai

Keynote

Author:

Daniel Wu

Course Facilitator
Stanford University

Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.

Daniel is passionate about the democratization of technology and the ethical use of AI - a philosophy he shares in the computer science and AI/ML education programs he has contributed to over the years.

Daniel Wu

Course Facilitator
Stanford University

Daniel Wu is a technical leader who brings more than 20 years of expertise in software engineering, AI/ML, and high-impact team development. He is the Head of Commercial Banking AI and Machine Learning at JPMorgan Chase where he drives financial service transformation through AI innovation. His diverse professional background also includes building point of care expert systems for physicians to improve quality of care, co-founding an online personal finance marketplace, and building an online real estate brokerage platform.

Daniel is passionate about the democratization of technology and the ethical use of AI - a philosophy he shares in the computer science and AI/ML education programs he has contributed to over the years.

[BUSINESS LEADER TRACK] PANEL: Pre-Training and Finetuning: how to train LLMs in an efficient, effective, and affordable manner

Author:

Saeed Contractor

Global Head of Automated Intelligence
Uber

Saeed Contractor is the Global Head of the Intelligent Automation COE, Tech at Uber. He leads the Intelligent Automation COE at Uber for Technology / Architecture, Strategy and Implementation. Recognized as a hands-on leader of software Architecture and Development, Saeed brings together new Technologies, Engineering, Business Processes and Mathematics to provide innovative and effective solutions to difficult problems. He has a strong customer focus and drives the Agile development of secure, scalable, reliable and highly available products with due consideration of negative paths and incorporating feedback from all stages of the product life cycle. Saeed has a Master of Engineering Degree from Princeton University and an MBA from UCF.

Saeed Contractor

Global Head of Automated Intelligence
Uber

Saeed Contractor is the Global Head of the Intelligent Automation COE, Tech at Uber. He leads the Intelligent Automation COE at Uber for Technology / Architecture, Strategy and Implementation. Recognized as a hands-on leader of software Architecture and Development, Saeed brings together new Technologies, Engineering, Business Processes and Mathematics to provide innovative and effective solutions to difficult problems. He has a strong customer focus and drives the Agile development of secure, scalable, reliable and highly available products with due consideration of negative paths and incorporating feedback from all stages of the product life cycle. Saeed has a Master of Engineering Degree from Princeton University and an MBA from UCF.

Author:

Shreesha Jagadeesh

Associate Director of Applied Machine Learning
BestBuy

Shreesha Jagadeesh is an Associate Director of Machine Learning at Best Buy. He leads a multi-national team of ML Scientists and Engineers building models that power the online customer journey through personalized recommendations and ads. He leverages his expertise in Multi-Stage Recommender Systems, LLMs, Embeddings, Multi-Arm Bandits, Offline Policy Evaluation and A/B testing to help digital teams to personalize the experiences deepening customer relationship & driving ecommerce revenue for Best Buy.

 

Prior to Best Buy, he has worked in a variety of corporate and consulting roles including at Amazon, EY and Cisco building Data Science models in a diverse set of domains spanning HR, Tax, Legal and Supply Chain. Outside of his day job, he advises early-stage startup, reviews pre-publication books/courses and has also published 2 online Data Science courses. He lives in Boston with his wife and enjoys travelling to exotic locations with an Antarctica expedition coming up in December 2024.

Shreesha Jagadeesh

Associate Director of Applied Machine Learning
BestBuy

Shreesha Jagadeesh is an Associate Director of Machine Learning at Best Buy. He leads a multi-national team of ML Scientists and Engineers building models that power the online customer journey through personalized recommendations and ads. He leverages his expertise in Multi-Stage Recommender Systems, LLMs, Embeddings, Multi-Arm Bandits, Offline Policy Evaluation and A/B testing to help digital teams to personalize the experiences deepening customer relationship & driving ecommerce revenue for Best Buy.

 

Prior to Best Buy, he has worked in a variety of corporate and consulting roles including at Amazon, EY and Cisco building Data Science models in a diverse set of domains spanning HR, Tax, Legal and Supply Chain. Outside of his day job, he advises early-stage startup, reviews pre-publication books/courses and has also published 2 online Data Science courses. He lives in Boston with his wife and enjoys travelling to exotic locations with an Antarctica expedition coming up in December 2024.

Author:

Stavros Zervoudakis

Professor at NYU, and VP of AI
Mutual of America

With over two decades of experience, Stavros Zervoudakis has established himself as a forward-thinking AI leader, sculpting the future with innovative solutions. He boasts an impressive track record, having orchestrated the successful launch of 24 AI initiatives in just three years, including the implementation of a comprehensive Data Science, Analytics, and Machine Learning framework. His work has catalyzed monumental gains, with single projects injecting between $12M and $56M in value.   Recently, Stavros has architected and deployed a groundbreaking, modular Generative AI platform, enriching the digital ecosystem with four pioneering GenAI products seamlessly integrated across six input/output systems. At the helm of his company's AI voyage, he not only crafted the strategic blueprint but also executed the firm's inaugural AI/ML model in a mere three months, netting an impressive $450K in savings.   A natural leader, Stavros has guided teams towards the creation of business-transforming applications, leveraging the latest in technology through agile, SME-centric project methodologies. His tenure as an academic luminary at NYU and the University of Exeter has allowed him to disseminate his profound AI insights, nurturing the next wave of innovation.   His journey with agile startups and industry titans like Citigroup, led him to deep-rooted understanding of regulatory mandates and has empowered him to weave essential privacy and security measures into his strategic visions, product leadership, and advisory roles, benefiting startups and Fortune 500 firms.   Beyond the digital realm, Stavros is a connoisseur of photography, an avid reader, a globetrotter, and a passionate creator of a future Einstein. Connect with him on LinkedIn to explore common interests.

Stavros Zervoudakis

Professor at NYU, and VP of AI
Mutual of America

With over two decades of experience, Stavros Zervoudakis has established himself as a forward-thinking AI leader, sculpting the future with innovative solutions. He boasts an impressive track record, having orchestrated the successful launch of 24 AI initiatives in just three years, including the implementation of a comprehensive Data Science, Analytics, and Machine Learning framework. His work has catalyzed monumental gains, with single projects injecting between $12M and $56M in value.   Recently, Stavros has architected and deployed a groundbreaking, modular Generative AI platform, enriching the digital ecosystem with four pioneering GenAI products seamlessly integrated across six input/output systems. At the helm of his company's AI voyage, he not only crafted the strategic blueprint but also executed the firm's inaugural AI/ML model in a mere three months, netting an impressive $450K in savings.   A natural leader, Stavros has guided teams towards the creation of business-transforming applications, leveraging the latest in technology through agile, SME-centric project methodologies. His tenure as an academic luminary at NYU and the University of Exeter has allowed him to disseminate his profound AI insights, nurturing the next wave of innovation.   His journey with agile startups and industry titans like Citigroup, led him to deep-rooted understanding of regulatory mandates and has empowered him to weave essential privacy and security measures into his strategic visions, product leadership, and advisory roles, benefiting startups and Fortune 500 firms.   Beyond the digital realm, Stavros is a connoisseur of photography, an avid reader, a globetrotter, and a passionate creator of a future Einstein. Connect with him on LinkedIn to explore common interests.

Author:

Hemant Jain

Senior Software Engineer, Inference
Cohere

Hemant works on efficiently fine-tuning and serving LLMs as a part of the Platform team at Cohere. Prior to this, he spent 3 years at NVIDIA developing Triton Inference Server, an open-source solution used to deploy machine learning models into production. He has a Masters in Data Science from the University of Washington.

Hemant Jain

Senior Software Engineer, Inference
Cohere

Hemant works on efficiently fine-tuning and serving LLMs as a part of the Platform team at Cohere. Prior to this, he spent 3 years at NVIDIA developing Triton Inference Server, an open-source solution used to deploy machine learning models into production. He has a Masters in Data Science from the University of Washington.

[BUSINESS LEADER TRACK] USE CASES: Generative AI use cases and implementation considerations

This session is targeted at technical leaders interested in generative AI applications. Donna and Kevin will cover business use cases leveraging generative AI, patterns for building common scenarios and best practices for implementation.

Author:

Donna Schut

Head of Technical Solutions Management, Generative AI & Large Scale ML
Google Cloud

Donna Schut

Head of Technical Solutions Management, Generative AI & Large Scale ML
Google Cloud

Author:

Kevin Tsai

Head of Technical Solution Architecture, Generative AI & ML Infrastructure
Google Cloud

Kevin currently leads a team of Solution Architects with focus on Generative AI and large-scale, accelerated infrastructure. Kevin has also led teams in Data Analytics, Data Management, and ML at Google, and has more than 20 years of experience in the technology industry

Kevin Tsai

Head of Technical Solution Architecture, Generative AI & ML Infrastructure
Google Cloud

Kevin currently leads a team of Solution Architects with focus on Generative AI and large-scale, accelerated infrastructure. Kevin has also led teams in Data Analytics, Data Management, and ML at Google, and has more than 20 years of experience in the technology industry