Generative AI with Big Data Training in Cecil Street, Singapore

The Generative AI with Big Data Training is designed to help professionals build, deploy, and scale Generative AI solutions on large-scale data platforms. The course bridges Big Data engineering with modern Generative AI and LLM workflows, focusing on data ingestion, processing, model integration, fine-tuning, and enterprise-ready architectures.

Introduction to Generative AI & Big Data

• Evolution of Big Data and AI convergence

• What is Generative AI and LLMs

• Enterprise use cases across industries

• Challenges of Generative AI at scale

Big Data Foundations for AI

• Data lakes, warehouses, and lakehouse architecture

• Batch vs streaming data pipelines

• Structured, semi-structured, and unstructured data

• Data quality and preprocessing for AI

Data Engineering for Generative AI

• Large-scale data ingestion pipelines

• ETL/ELT using Spark

• Feature engineering for LLM use cases

• Handling text, documents, images, and logs

Generative AI & LLM Fundamentals

• Transformer models overview

• Pre-trained vs fine-tuned LLMs

• Tokenization and embeddings

• Limitations and risks of LLMs

Prompt Engineering at Scale

• Prompt design principles

• Dynamic and contextual prompts

• Prompt templates using big data

• Reducing hallucinations with data grounding

Embeddings & Vector Databases

• Creating embeddings from large datasets

• Vector indexing and similarity search

• Integrating vector stores with data lakes

• Performance considerations at scale

Retrieval-Augmented Generation (RAG)

• RAG architecture and workflow

• Chunking and indexing strategies

• Integrating RAG with Spark pipelines

• Enterprise document intelligence systems

Fine-Tuning & Customization

• When to fine-tune vs prompt

• Fine-tuning using large datasets

• Distributed training concepts

• Cost and performance trade-offs

Real-Time & Streaming AI Pipelines

• Streaming data with AI inference

• Event-driven Generative AI use cases

• Near real-time decision systems

• Latency optimization strategies

Deployment & MLOps for Generative AI

• Model serving architectures

• CI/CD for AI pipelines

• Monitoring, logging, and observability

• Model drift and performance tracking

Security, Governance & Responsible AI

• Data privacy and compliance

• Access control and encryption

• Bias, fairness, and explainability

• Enterprise AI governance frameworks

Cloud & Platform Integration

• Generative AI on cloud data platforms

• Integrating AI services with Databricks

• Cost optimization strategies

• Scaling workloads efficiently

Case Studies

Hands-on Ecercises

Summary and Conclusion