Aishwarya has built one of the most trusted AI communities in the world with over 1 Million followers who rely on her to cut through the noice.

She spent a decade building AI at the companies that defined the modern stack Google, Microsoft, and IBM. As the head of AI Developer Relations at Fireworks AI, she worked directly at the interface businesses, builders and the infrastructure powering production-scale AI systems. She holds a Master's in Data Science from Columbia University.

Aishwarya's teaching philosophy is simple: explain the real thing, not the sanitised version. Her sessions are known for being brutally honest, technically sharp, and remarkably clear.

Arvind Narayan brings a rare cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe, where he led data science teams and developed production-grade ML solutions tied to significant ARR growth.

He currently serves as AI Solutions Architect at Ema, a leading AI startup focused on agentic enterprise transformation, and is Founder of Eikos Health, an agentic scheduling platform for doctor-led clinics.

Arvind holds a Master's from Carnegie Mellon University. His teaching style emphasizes practical thinking, strong mental models, and translating AI concepts into production-ready solutions.

Rami Krispin is a Senior Manager of Data Science and Engineering, a Docker Captain, and a

LinkedIn Learning Instructor with over a decade of experience building and leading data and AI

teams in production environments. Working at the intersection of machine learning, engineering,

and developer tooling, he brings a rare perspective on turning research-grade AI into systems

that teams can actually trust and businesses rely on.

In his work, Rami focuses on operationalizing AI, from forecasting and ML pipelines to SQL

agents and local LLM workflows, navigating the real challenges of model behavior, system

architecture, and safe access to critical data. He offers deep insight into how SQL AI agents are

evolving from flashy demos into production backbones, what separates teams that ship from

teams that stall, and what it truly takes to build trusted AI on top of live data.