Global Artificial Intelligence Virtual Conference- Webinar (Free)

Free Warm-up webinar on Nov 25th 1.00pm - 1.30pm PST

We are very excited to organize GAIV Conference - Dec 2025.

As we get closer to the conference, we want to invite you to participate in free Warm-Up Webinar on . Nov 25th (1.00pm - 1.30pm PST) - Tuesday.

Please start registering by entering your name and email address to attend Webinar

Warmup Webinar Speakers
Ankur Sharma (Principal Architect, Equinix)
Shelby Heinecke (Senior Manager. Salesforce)
Sakshi Naik (Data Scientist, IEEE)

Description

Topic : From Data to Decision-Makers: Architecting Trustworthy AI at Scale..
Ankur Sharma (Principal Architect, Equinix)

Abstract
AI’s promise lies not in isolated models but in its ability to transform how organizations make decisions. Yet, the gap between experimental AI projects and enterprise-wide adoption remains wide often due to fragmented data systems, opaque models, and lack of trust in automation. This talk explores how to architect AI ecosystems that move seamlessly from raw data to trusted decisions. Drawing from real-world experience building resilient multi-cloud and enterprise systems, we will examine the critical layers of a trustworthy AI architecture from data lineage and feature governance to explainable model pipelines and human-in-the-loop feedback systems. The session will also discuss operationalizing these principles at scale: embedding observability, transparency, and ethical guardrails directly into the AI lifecycle. Attendees will learn how to bridge the divide between data science experimentation and production-grade decision systems, how to balance automation with accountability, and how to design AI platforms that leaders can trust, not just use. The result is a framework for AI that is reliable, explainable, and actionable across the enterprise.


Profile
I’m Ankur Sharma. I’m a technology leader with deep experience across data centers, hybrid multi-cloud networking, time synchronization, and AI-driven systems. Over the years, I’ve led engineering teams spanning control plane, data plane, API, and UI development—focusing on building resilient and scalable enterprise platforms. My work has included innovations in orchestration, observability, and private interconnection, and I hold several patents in networking and timing for multi-cloud environments. I’m currently exploring how AI can be applied to networking and synchronization to enable greater automation, intelligence, and adaptive security. Earlier in my career, I also worked on technologies in social messaging, media, and bioinformatics.
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Topic: The Next Era of Enterprise AI: Small, Fast, Reliable
Shelby Heinecke (Senior Manager. Salesforce)

Abstract
In the enterprise, speed and reliability beat size every time. In this talk, I’ll share how my team at Salesforce has deployed lightweight models in our data platform, developed small action models, and built new enterprise-centric agent evaluations. These projects highlight the lessons driving today’s deployments and the research laying the foundation for what’s next.
Who is this presentation for?
Data + AI leaders, practitioners of all levels (junior, senior, lead), business leaders beginning to deploy AI


Profile
Dr. Shelby Heinecke is a Senior AI Research Manager at Salesforce, where she leads a team advancing AI agents, on-device AI, and small language models. She has authored 35+ AI research publications and driven multiple product deployments that expand what’s possible in enterprise AI. Shelby earned her Ph.D. in Mathematics from the University of Illinois at Chicago, after completing an M.S. at Northwestern University and a B.S. at MIT. She is passionate about building cutting-edge technology and leading the teams that bring it to life.
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Topic: Workshop: Agentic AI: How to Build Autonomous Data Workers That Don’t Go Rogue
Sakshi Naik (Data Scientist, IEEE)

Abstract
Imagine creating an AI worker that not only does the heavy lifting of data engineering but actually drives revenue for your organization — while you sleep. In this full-day hands-on workshop you’ll learn how to build autonomous data agents that connect data, analytics and business decisions, unlocking new income streams, cost savings and strategic advantage in the AI race. We’ll cover the architecture behind these revenue-enabled agents: how to structure them to follow business rules, track performance, generate measurable ROI, and avoid the trap of becoming just another “expensive toy.” Participants will walk away with a clear blueprint to deploy agents that: Automate data workflows, free up human engineers and reduce costs. Generate business value by feeding insights, making decisions and executing tasks (e.g., identify new opportunities, market segments, or operational inefficiencies). Monetize their activity via pricing models (usage-based, outcome-based, seat-based) that tie your architecture to income rather than just “cool tech.” Split into two sessions (4 hrs each): Session 1 (Morning): Foundations of agentic data systems — autonomy, memory, decision logic, value-tracking, business alignment. Session 2 (Afternoon): Hands-on building of an agentic data worker from ingestion to insight to action — including how to instrument it for monetizable outcomes like cost savings, lead generation or process optimization. By the end of the day you’ll walk away not just with code, but with a monetization map: how to position your agentic system for profit, how to communicate its business case, and how to stay ahead in the evolving AI race.

Profile
Sakshi Naik is an AI researcher, data architect, and IEEE leader recognized nationally for her work on building ethical and reliable AI systems. She currently chairs the IEEE USA Agentic AI Subcommittee, where she helps shape U.S. policy positions on responsible automation and intelligent systems. With over five years of industry experience at Walgreens, Sakshi has engineered large-scale data and AI platforms on Azure Databricks, driving analytics modernization and end-to-end automation across enterprise environments. Her technical expertise spans data engineering, AI fairness frameworks, and scalable ML infrastructure. Beyond her professional roles, Sakshi is a speaker at global AI and data conferences including ODSC West, PASS Data Summit, AI Infra Summit, and the C# Software Architecture Conference, where she has addressed thousands on the future of trustworthy AI. She also mentors students and early-career technologists through IEEE and her YouTube series “AI with Sakshi.” Sakshi’s mission is to make AI both powerful and principled — systems that help people, not replace them.

Check Global Artificial Intelligence Virtual Conference Dec 15-17 speakers information:

https://www.globalbigdataconference.com/virtual/global-artificial-intelligence-conference/event-146.html

https://www.globalbigdataconference.com/virtual/global-artificial-intelligence-conference/speakers-146.html