Practitioners and leaders exploring practical AI systems, adoption, governance, security, and analytics.
12 sessions from the real MITechCon schedule
Conversational BI fails when answers are not trusted. Self-service analytics only works when the data, metrics, and context behind each answer are governed. In "Designing Conversational BI: How Azure Databricks AI/BI Genie Unlocks Self-Service Insights", Mou.
AI agents can damage production data before a human sees the mistake. No confirmation prompt fired, no override path existed, and the database changed before anyone could intervene. In "Your AI Agent Just Ran DELETE Without WHERE. Now What?", Jonathan.
Enterprise investigations slow down when evidence is scattered across systems. The challenge is bringing retrieval, agent workflow, and human oversight together without burying investigators in more noise. In "Case Study: Designing AI-Augmented Enterprise.
Prompting is only one layer of useful AI. Once an LLM feature reaches real users, the engineering questions move from prompts to behavior, context, risk, and reliability. "LLMs Under the Hood - Applied Engineering Workshop" lets Barry Stahl connect tokens.
Finance AI breaks when metrics and pipelines are not trusted. Before finance can safely use AI or agentic workflows, the data foundation has to be trusted, governed, and explainable. The answer gets practical in "Building an AI-Ready Finance Lakehouse with.
Sepsis care depends on fast signals across fragmented workflows. The hard part is keeping trust intact as volume, ownership, definitions, and expectations change. In "AI and Digital Transformation in Sepsis Management for Smarter Healthcare Delivery", Swapna.
AI-powered vision can improve quality control only when it adapts to real production conditions. Quality systems have to respond to variation on the line, not just perform in a clean lab setup. "AI Influence on Reliable Machine Vision" lets Michael Koper.
Static rules create false positives and miss changing crime patterns. The hard part is keeping trust intact as volume, ownership, definitions, and expectations change. In "Applying Machine Learning and Network Analytics for Modern Financial Crime Detection".
RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun.
Multi-agent systems get slow and expensive when every agent sees everything. Once the idea reaches a real workflow, the hard questions shift to context, trust, cost, and reliability. In "Multi-Agent Without the Bloat: Designing Lean AI Systems on Azure".
Pipeline failures should not wait for someone to notice a bad report. Every data engineer knows the 2 AM pipeline failure — the one nobody notices until Friday's report is wrong. "Agentic Loops in the Data Stack: From Pipeline Failure to Auto-Remediation".
Agentic AI can be technically correct and business-wrong. Agents need business meaning, not just access to more tools and data. In "Semantic Layers: The Missing Foundation for Agentic AI in Enterprise", Sajitha Sritharan shows why semantic layers, knowledge.
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Speakers connected to the sessions in this guide
Mou RakshitAvanade, Intelligent Data Platform Data Engineering Thought leadership2 sessions in this guide
Varun JoshiSenior Data Engineer at AWS2 sessions in this guide
Barry StahlSolution Architect and Developer1 session in this guide
Carey PayetteFinancial Finesse1 session in this guide
John Selvaraj ArulappanLead AI Engineer @ADP1 session in this guide
Jonathan StewartJonathan Stewart aka SQLLocks, Principal Analytics Consultant for SoundBI1 session in this guide
Michael KoperDigital Vision Manager - Americas1 session in this guide
Sajitha SritharanSenior Cloud Data Architect | Microsoft Certified | Women in Data Advocate1 session in this guide
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