AI-3022: Implement knowledge mining with Azure AI Search

Course Description

Unlock the full potential of your enterprise data by learning to implement knowledge mining with Azure AI Search. This intermediate-level, instructor-led course guides developers and AI engineers through building intelligent search solutions that extract insights from structured and unstructured content. You’ll explore AI enrichment pipelines, custom skills, semantic and vector retrieval, and knowledge store integration—all to enhance search relevancy and data discoverability.

Target Audience

This course is ideal for:

• AI Engineers, Software Developers, and Solution Architects building knowledge mining and AI search solutions

• Data Engineers and IT Professionals seeking to extract insights from complex data sets

• Professionals proficient in C# or Python with a working understanding of Azure fundamentals

Prerequisites:

• Familiarity with Azure and application development (C# or Python)

Course Outline

Module 1: Build an Azure AI Search Solution

• Provision and configure Azure AI Search service

• Model and index structured/unstructured data

• Implement search queries, filters, and sorting to deliver relevant results

Module 2: Create Custom Skills for AI Enrichment

• Develop custom AI skills to enrich documents (e.g., classification, entity extraction)

• Integrate custom skills into enrichment pipelines for advanced insight

Module 3: Build Knowledge Store Pipelines

• Create a knowledge store to persist AI-enriched content

• Project enriched data for downstream analytics and applications

Module 4: Implement Advanced Search Features

• Enhance search relevance using term boosting, scoring profiles, and multilingual analyzers

• Incorporate filters and geographic proximity search for refined results

Module 5: Index External Data via Azure Data Factory

• Use Azure Data Factory to ingest external data sources

• Populate search indexes using Data Factory’s integration capabilities

Module 6: Maintain and Optimize Azure AI Search Solutions

• Monitor performance and cost-efficiency

• Enhance reliability, security, and debugging processes in production environments

Module 7: Apply Semantic Ranking for Enhanced Relevance

• Leverage semantic ranking (L2 ranking) to improve document relevance

• Configure semantic search to support natural-language queries

Module 8: Implement Vector Search and Retrieval

• Introduce vector embeddings for similarity-based retrieval

• Perform vector search using embedding models for enhanced knowledge mining

HandsOn Experience

Approximately 40–50% of the course time is hands-on, featuring guided demos and code walkthroughs to build and deploy real-world knowledge mining solutions with Azure AI Search.

Skills You’ll Gain

By completing AI3022, you’ll be able to:

• Architect and deploy AI search pipelines that enrich and index data

• Develop custom skills to enhance search with AI-driven insights

• Build and query knowledge stores for structured content extraction

• Improve search relevance through semantic and vector techniques

• Integrate external datasets and optimize search performance in Azure