Real-Time Data Streams in Generative AI Use Cases

Feed live data into GenAI systems for up-to-date responses.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course covers the integration and management of real-time data streams in Generative AI applications. Participants will learn how to connect live data sources to GenAI models to enable dynamic, context-aware AI outputs that reflect the latest information. The course also addresses technical challenges and governance considerations when handling streaming data.

Learning Objectives

Participants will be able to:

• Understand the importance of real-time data for Generative AI relevance

• Design architectures for ingesting and processing streaming data

• Integrate live data feeds into GenAI workflows effectively

• Address latency, scalability, and reliability challenges

• Ensure governance and compliance for real-time data use

Target Audience

• Data engineers and architects

• AI solution developers

• IT infrastructure and operations teams

• Business analysts leveraging GenAI insights

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructor-led sessions on streaming data concepts

• Hands-on labs connecting data streams to AI models

• Case studies showcasing real-time AI applications

• Group exercises on problem-solving in data pipelines

Materials Provided

• Sample code for streaming data ingestion

• Architecture templates for real-time GenAI systems

• Best practice checklists for performance and governance

Outcomes

• Ability to design and implement real-time data pipelines for GenAI

• Enhanced AI model responsiveness with up-to-date information

• Improved decision-making using live data-driven AI outputs

• Practical skills to manage technical and regulatory aspects of streaming data

Outline / Content

Day 1: Introduction to Real-Time Data and GenAI

• Overview of real-time data types and sources

• Benefits and use cases for live data in GenAI

Day 2: Architecting Real-Time Data Pipelines

• Technologies and frameworks for streaming data (Kafka, Flink, etc.)

• Integrating streams with GenAI models

Day 3: Ensuring Performance and Scalability

• Managing latency and throughput

• Handling failures and ensuring reliability

Day 4: Governance, Compliance, and Use Case Implementation

• Data privacy and security in real-time streams

• Case study workshop: Deploying a real-time GenAI application