Event Format
Duration: 10:00 AM – 4:00 PM IST
Format: In-person
Registration: Open for registration till 14th Feb 2026
Final Attendance: Invite-only confirmation (based on relevance and capacity)
Location: Will be shared only with confirmed participants
Register here: https://forms.gle/4uHZNKxBH7xcCwEU6

Event Overview
Generative AI is rapidly evolving from experimentation to enterprise adoption. However, building reliable, secure, and scalable GenAI solutions requires strong data foundations, governance, and architecture — not just access to large language models.

This full-day session focuses on how Databricks enables organisations to move from raw data to governed intelligence using Generative AI. The session is designed to be practical, architecture-driven, and grounded in real-world enterprise use cases.

While registration is open to all, final participation will be confirmed via invitation to ensure a focused and high-quality learning environment.

Who Should Attend
This event is open to:

• Data Engineers
• Data Scientists
• ML / AI Engineers
• Analytics Engineers
• BI professionals exploring GenAI
• Final-year students (Data, AI, or Engineering backgrounds)

Note: This is not an introductory AI session. Participants are expected to have basic familiarity with data or analytics concepts.

Full-Day Agenda (10:00 AM – 4:00 PM)

10:00 – 10:20 | Welcome and Session Orientation

• Event objectives and structure
• How the day will progress
• What participants should expect by the end of the session

10:20 – 11:00 | GenAI in the Enterprise: Reality vs Hype

• Why GenAI initiatives fail without strong data foundations
• Why ChatGPT-style demos don’t translate to enterprise success
• Common challenges such as hallucinations, data leakage, and lack of governance
• Why GenAI is a data engineering and ML problem, not just prompting
• Where Databricks fits in the enterprise GenAI landscape
• 11:00 – 11:45 | Databricks GenAI Reference Architecture
• Lakehouse architecture for GenAI workloads
• Delta Lake as a reliable and auditable data layer
• Feature engineering and data preparation for AI
• Vector search and embedding workflows
• Unity Catalog for governance and access control
• MLflow for experiment tracking and model lifecycle

11:45 – 12:00 | Break

12:00 – 12:45 | Core GenAI Concepts and Design Patterns

• How production-grade GenAI systems are built
• LLMs (OpenAI, Azure OpenAI, and open-source models)
• Embeddings and semantic similarity
• Retrieval-Augmented Generation (RAG)
• Prompt versioning, evaluation, and observability
• Structured versus unstructured GenAI use cases

Real-world examples include:

• Internal knowledge assistants
• Compliance and policy search
• GenAI over enterprise BI data

12:45 – 1:30 | Lunch Break

1:30 – 2:45 | Live Demo: Building GenAI on Databricks

• Ingesting enterprise data into Delta Lake
• Creating embeddings and vector indexes
• Implementing a RAG pipeline
• Querying data using an LLM
• Tracking experiments with MLflow
• Applying governance using Unity Catalog

Focus will be on architecture, data flow, and decision-making rather than UI demonstrations.

2:45 – 3:00 | Break

3:00 – 3:30 | Governance, Security and Cost Control

• What makes GenAI enterprise-ready
• Role-based access control for GenAI systems
• Handling sensitive and regulated data
• Monitoring, observability, and model drift
• Cost optimisation strategies
• Why Databricks is safer than ad-hoc GenAI stacks

3:30 – 3:50 | Career and Industry Mapping

• GenAI roles, skills, and expectations
• Data Engineer vs ML Engineer vs AI Engineer
• Skill expectations for GenAI projects
• Portfolio and project guidance
• What not to over-focus on, such as prompt-only roles

3:50 – 4:00 | Q and A and Closing

Learning Outcomes
Participants will:

• Understand enterprise GenAI architecture on Databricks
• Learn how Lakehouse and GenAI work together
• Gain clarity on RAG, embeddings, vector search, and governance
• See a real-world GenAI implementation end to end
• Understand career pathways in GenAI

Prerequisites

• Basic understanding of data pipelines or analytics
• Familiarity with SQL or Python is beneficial
• Willingness to think beyond GenAI demos

Registration and Selection Process

• Registration is open to all
• Participants must fill out the registration form
• Final participation will be confirmed via email invitation
• Seats are limited to maintain quality and interaction

Register here: https://forms.gle/4uHZNKxBH7xcCwEU6