Scalable Data Pipelines for Generative AI Workloads

Automate ingestion, processing, and delivery of data to GenAI tools.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course explores the design and implementation of scalable data pipelines tailored for Generative AI workloads. Participants will learn how to automate data collection, transformation, and delivery processes to ensure efficient and reliable data flow into GenAI systems. Emphasis is placed on handling large volumes of data, maintaining data quality, and optimizing performance for AI training and inference.

Learning Objectives

Participants will be able to:

• Understand the architecture of scalable data pipelines for AI workloads

• Automate ingestion from diverse data sources into a unified system

• Implement data transformation and enrichment for GenAI readiness

• Ensure pipeline reliability, fault tolerance, and monitoring

• Optimize pipelines for performance and cost efficiency

Target Audience

• Data engineers and pipeline developers

• AI/ML engineers and data scientists

• IT infrastructure and DevOps teams

• Business analysts interested in AI data workflows

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructor-led lectures on pipeline architectures

• Hands-on labs building scalable data ingestion workflows

• Case studies on successful GenAI data pipeline implementations

• Group discussions on challenges and best practices

Materials Provided

• Pipeline design templates and automation scripts

• Sample datasets and integration guides

• Monitoring and troubleshooting checklists

Outcomes

• Ability to design and deploy scalable data pipelines for GenAI

• Improved data flow automation and management for AI projects

• Enhanced data quality and availability for training and inference

• Practical knowledge of monitoring and optimizing pipelines

Outline / Content

Day 1: Fundamentals of Scalable Data Pipelines

• Overview of pipeline components and architectures

• Data sources and ingestion methods

Day 2: Automation and Processing Techniques

• ETL/ELT workflows and data transformation

• Data enrichment and augmentation for GenAI

Day 3: Ensuring Reliability and Monitoring

• Fault tolerance and error handling

• Tools for pipeline monitoring and alerting

Day 4: Optimization and Real-World Applications

• Performance tuning and cost management

• Case study workshop: Building a pipeline for a GenAI use case