The right database for your AI applications

Module 1

Fundamentals: Understanding Data and AI

• Understanding the Basic Principle of AI: How Does a Machine Learn from Data and Become Intelligent as a Result? 
• Why data is the foundation of every AI application, and why projects without a solid data foundation fail.
• Defining Key Terms: What Are Structured, Unstructured, and Semistructured Data?
• What data formats are available, and which ones are best suited for what?

Data Quality: The Decisive Factor

• How to Spot Poor Data Quality and What It Means for AI Results.
• The most important quality criteria: completeness, consistency, and timeliness.
• Bias in Data: How Bias Arises and How to Spot It.
• Ensuring data quality in ongoing AI applications.

Prepare and Consolidate Data

• How Data from Different Sources Is Brought Together: The ETL Principle—Extract, Transform, and Load—Explained in Simple Terms.
• Data Splitting: Why AI Models Need Different Datasets (training, validation, and test data).

Module 2

Storing Data: Key Concepts

• An Overview of Database Types: Relational Databases, Data Lakes, Data Warehouses.
• Vector Databases and Knowledge Graphs: What They Are and Why Modern AI Applications Need Them.
• RAG Explained (Retrieval-Augmented Generation): How AI accesses a company's own data rather than just general knowledge.

Cloud vs. On-Premises: The Most Important Decision-Making Criteria at a Glance.

Classify and Support AI Projects

• The typical phases of an AI project: From the initial idea through the first test (proof of concept) to ongoing operation.
• Who does what? Data Engineer, Data Scientist, ML Engineer—so you know who to turn to and where to start.

Legal Guidelines

• GDPR and Personal Data: What You Need to Know When Working on an AI Project.
• EU AI Act: What the law requires and what it means for data-driven projects.
• Data Sovereignty in Practice: When Local Models Make Sense.