AI Data Pipelines and Knowledge Systems

Design and connect AI data pipelines for modern knowledge systems

Behind every AI assistant, smart search engine, or enterprise copilot sits a powerful data pipeline.

On this online course, you’ll learn how to architect, optimise, and connect those pipelines to deliver reliable, context-aware AI applications in real-world organisations.

You’ll begin with the foundations of AI data pipeline architecture, progress to implementing retrieval-augmented generation (RAG) systems for enterprise use, and finish by bridging data engineering with AI innovation to create intelligent, scalable knowledge systems.

Grasp pipeline architecture

Start by exploring the core principles of data engineering for AI. Learn how to manage ingestion, preprocessing, embedding, and transformation with a focus on modularity, reliability, and scalability.

You’ll connect workflows to vector databases and GenAI models to ensure retrieval is robust, responsive, and accurate at scale.

Work with RAG enterprise solutions

With that foundation backing you, you’ll move on to designing enterprise-grade RAG systems that support real-world applications, such as customer support chatbots and knowledge management platforms.

Build document pipelines, knowledge bases, and retrieval enhancements like metadata filtering and reranking, while practising optimisation for performance and accuracy.

Bridge data engineering with AI innovation

You’ll wrap this course up by combining robust pipelines with adaptable GenAI models to create intelligent support and knowledge systems that grow with organisational needs.

Learn how to monitor, evaluate, and maintain these systems so they deliver accurate, up-to-date information while transforming enterprise access to knowledge.

This course is ideal for data and machine learning engineers, software specialists, and technical architects building robust data pipelines, knowledge management platforms, and retrieval-augmented GenAI enterprise systems.

This course is ideal for data and machine learning engineers, software specialists, and technical architects building robust data pipelines, knowledge management platforms, and retrieval-augmented GenAI enterprise systems.

• Construct robust data processing pipelines that transform raw data into AI-ready formats
• Implement advanced RAG architectures with component integration and performance optimization
• Develop customer support RAG systems with domain-specific knowledge base management
• Apply advanced retrieval strategies including metadata filtering, reranking, and quality enhancement