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.
Advanced Data Techniques for Enterprise AI Systems
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Advanced Data Techniques for Enterprise AI Systems
This course is part of Modern Data Strategy for Enterprise Generative AI Specialization
Instructors: David Drummond
Included with
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explain the foundational role of data management, security, and architecture in enterprise AI.
Implement cross-platform compatibility across AI models, datasets, and systems.
Apply vector database and embedding techniques to manage unstructured data.
Build unified data architectures using Iceberg, Delta, and DuckLake.
Skills you'll gain
- Generative AI Agents
- Data Management
- Docker (Software)
- Data Storage
- Metadata Management
- Data Store
- Real Time Data
- Data Strategy
- Machine Learning
- Semantic Web
- Data Architecture
- Data Loss Prevention
- Data Ethics
- Data Security
- Interoperability
- Agentic systems
- Large Language Modeling
- Data Governance
- Responsible AI
- Data Processing
Details to know
Shareable certificate
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Recently updated!
September 2025
Assessments
12 assignments
Taught in English
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This course is part of the Modern Data Strategy for Enterprise Generative AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 6 modules in this course
Understand the pillars of modern data strategy—management, frameworks, and governance. Learn how to secure data using encryption, access control, and ISO standards.
What's included
4 videos2 readings2 assignments
Master interoperability across clouds and platforms using Docker, Kubernetes, APIs, and the SECURE framework to build scalable, resilient AI systems.
What's included
5 videos1 reading2 assignments1 ungraded lab
Organize and retrieve data efficiently using metadata tagging. Learn how tagging powers Retrieval-Augmented Generation (RAG) and enhances AI accuracy.
What's included
6 videos2 assignments1 ungraded lab
Explore vector databases for semantic and multimodal search. Learn about indexing strategies, ANN algorithms, and hardware acceleration for real-time AI.
What's included
8 videos4 readings2 assignments1 ungraded lab
Dive into data lakes, warehouses, and lakehouses. Use tools like DuckLake and Databricks to unify structured and unstructured data for Gen AI.
What's included
8 videos2 readings2 assignments1 ungraded lab
Apply governance frameworks like DAMA-DMBOK and EDM. Implement Explainable AI, Zero Trust Architecture, and Data Loss Prevention for secure, ethical AI systems.
What's included
6 videos1 reading2 assignments
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Frequently asked questions
It’s a practitioner’s path to enterprise‑grade GenAI via strong data frameworks and governance—critical for reducing hallucinations, ensuring compliance, and scaling LLM/RAG systems reliably.
Professionals responsible for data platforms, AI product delivery, and governance—Data/ML Engineers, Architects, Product Managers, and Data Stewards
Design and implement a comprehensive data framework, evaluate governance against Responsible AI criteria, and operationalize RAG/LLM solutions with measurable data quality.
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Financial aid available,