Enterprise end users | Kisaco Research

Enterprise end users

Enterprise Generative AI Summit
23-24 January, 2024
Florida, USA

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.

If you'd like to find out more information about attending as an AI vendors, register your interest here.

CONFIRM YOUR PLACE HERE

Featured Speakers Include

 

Daniel Wu

Head of AI & ML, Commercial Banking
JP Morgan Chase

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.

Read full bio

Daniel Wu

Head of AI & ML, Commercial Banking
JP Morgan Chase

Daniel Wu

Head of AI & ML, Commercial Banking
JP Morgan Chase

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.

Read full bio

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

VIEW FULL SPEAKER LINE-UP

Agenda highlights

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OPENING KEYNOTE: The ethical implications of generative ai

Keynote

Author:

Daniel Wu

Head of AI & ML, Commercial Banking
JP Morgan Chase

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.

... read more

Daniel Wu

Head of AI & ML, Commercial Banking
JP Morgan Chase

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.

... read more

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.

... read more

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

Vice President of Artificial Intelligence
Mutual Financial
... read more

Stavros Zervoudakis

Vice President of Artificial Intelligence
Mutual Financial

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.

... read more

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
... read more

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

... read more

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

VIEW THE FULL AGENDA

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