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AI/ML Discussion Forum

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This video provides a recap of the AI/ML Discussion Forum from 2/8/24 at 8:00 am PST.

Thank you for joining

We look forward to seeing you at our next forum.

Discuss the latest developments in Generative AI, including ChatGPT, and how they are propelling a huge interest by the business – not just as a technology or business tool but as a general product technology.


Representatives from Microsoft and Google Cloud will also be providing updates on their AI/ML platforms and capabilities.

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We look forward to seeing you there.

Join industry leaders in discussion topics around:

Discussion Topics
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Addressing Security concerns related data privacy

Security when using AI solutions in the cloud is a major concern for companies especially those  with proprietary tech, which limits those tools available - on-prem versus cloud options. What have other organizations done to address this without limiting the functionality?

Addressing the need of skill development

LLMs and generative AI are going to have a big impact on technical users. The traditional complaint in data science is that a lot of the actual work is basic data prep. The promise of this new AI era is that a lot of the grunt work will be automated by smarter AI tools. So how will data engineering evolve, will data scientists have more flexibility, how do BI analysts grow the skills required, and how can developers be more efficient?

How are companies measuring the ROI when communicating to the business?

How do we measure the ROI of AI investments to understand the value it brings to business. How do we ensure key metrics such as cost savings, revenue increase, efficiency gains, and customer satisfaction through AI, to ultimately make improved data-driven decisions. How have businesses from across various industries already successfully leveraged AI to measure and maximize their ROI?

Where does the AI/ML CoE reside and is it part of it or business or IT?

Does an AI/ML Center of Excellence (CoE) fall into the business or IT side of an organization? Or both? What are the benefits or challenges that companies may face in establishing an AI/ML COE?

What challenges are companies facing when implementing AI/ML and solution and how do they overcome it?

From simply not understanding the technology to gaining business support and trust, there are numerous struggles leaders are facing while attempting to adopt artificial intelligence into their processes. What are they and how do business leaders overcome them?

We will have “drop-in” presentations from our partners Google Cloud and Microsoft to share the latest updates on their AI and Open AI capabilities.

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Topic Leaders

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Brian Bare

Vice President of Global Data & Analytics and Enterprise Architecture

Discussion Topic:
Establishing an AI/ML Center of Excellence

With over twenty years of Data & Analytics experience, Brian heads OldCastle’s Global Data & Analytics and Enterprise Architecture teams.

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Kevin Hearn

SVP, Head of Consumer Bank Development

Discussion Topic:
Security Considerations for AI/ML

Kevin leads Axos Bank’s Consumer Bank Development teams and has expertise across supply chain distribution, retail, finTech and chemical manufacturing industries.

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Lee Hansen

Director, Specialist Data and AI 

Discussion Topic:
AI/ML at the Core of Digital Transformation

With over twenty years of technology sales experience, Lee works with Microsoft’s enterprise customers on their Data, Analytics, and AI pursuits. Prior to joining Microsoft, Lee guided some of the world’s most innovative companies in their AI journeys, leading sales efforts at early-stage organizations including Scale AI, CognitiveScale, and Narrative Science.

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Blake Lanning

AI/ML Solution Engineer

Discussion Topic:
How to Use GenAI to Unlock Value for your Organization

With over twenty years of technology experience, Blake is an Analytics leader driving digital transformation through data centricity, modern architecture, and business capability enablement in areas of digital marketing, financial, and non-financial domains.

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Ankit Virmani

Sr. Cloud Data Architect, Data and AI Enthusiast

Discussion Topic:
Bias and Fairness in AI

Ankit is an AI/Data enthusiast who loves sharing his knowledge about ethical AI, data engineering and data governance. He has extensive work experience in designing and developing petabyte scale data and ML platforms, working for companies like Google, Amazon, Deloitte. Ankit is a fellow at AI 2030, and speaks at prestigious business schools in North America on how AI should be used ethically and responsibly.


According to Gartner:


“By 2026, organizations that operationalize AI transparency, trust and security will see their AI models achieve a 50% improvement in terms of adoption, business goals and user acceptance”.


“By 2026, enterprises that have adopted AI engineering practices to build and manage adaptive AI systems will outperform their peers in the number and time it takes to operationalize AI models by at least 25%”.


“By 2027, at least one global company will see its AI deployment banned by a regulator for noncompliance with data protection or AI governance legislation”.

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