Generative AI with Big Data Training in Madrid, Spain

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