Advanced Data Techniques for Enterprise AI Systems

Overview

Generative AI succeeds or fails on the quality of your data strategy. In this hands on course, you’ll learn how to design scalable data frameworks and governance models that power LLMs, RAG, and agentic AI with reliable, ethical, and context rich information. The curriculum covers modern data strategy fundamentals, taxonomy design, and responsible AI practices—equipping you to reduce hallucinations, enforce compliance, and accelerate delivery of production ready AI solutions. Through case studies, interactive dialogues, labs, and practice assignments, you’ll apply taxonomies, metadata, and data quality controls to real world scenarios. By the end, you’ll be able to architect enterprise data foundations that make GenAI robust, explainable, and future proof.

Syllabus

Foundation and Strategy with Security

Understand the pillars of modern data strategy—management, frameworks, and governance. Learn how to secure data using encryption, access control, and ISO standards.

Cross-platform Compatibility and Interoperability

Master interoperability across clouds and platforms using Docker, Kubernetes, APIs, and the SECURE framework to build scalable, resilient AI systems.

Data Classification and Tagging

Organize and retrieve data efficiently using metadata tagging. Learn how tagging powers Retrieval-Augmented Generation (RAG) and enhances AI accuracy.

Modern Data Storage and Vector Processing

Explore vector databases for semantic and multimodal search. Learn about indexing strategies, ANN algorithms, and hardware acceleration for real-time AI.

Unified Data Management and Architecture Patterns

Dive into data lakes, warehouses, and lakehouses. Use tools like DuckLake and Databricks to unify structured and unstructured data for Gen AI.

Advanced Frameworks and Techniques

Apply governance frameworks like DAMA-DMBOK and EDM. Implement Explainable AI, Zero Trust Architecture, and Data Loss Prevention for secure, ethical AI systems.