AI-3022: Implement knowledge mining with Azure AI Search

AI-3022 Course: Implement knowledge mining with Azure AI Search

To earn this Microsoft Applied Skills credential, students must demonstrate the ability to create Azure AI Search solutions, implement custom skillsets, and add skill enrichment to an index to optimize search solutions.

Do you have information locked in structured and unstructured data sources? With this course, you will learn how, with Azure AI Search, you can extract key information from this data and allow applications to search and analyze it.

Level: Intermediate - Role: AI Engineer, Developer, Solutions Architect, Student - Product: Azure, Azure AI Search - Subject: Artificial Intelligence

Course aimed at

• Artificial intelligence engineers
• Cloud solutions developers
• Data architects
• Enterprise search professionals
• Data analysts working with large volumes of unstructured information
• Technical teams that wish to implement intelligent search solutions in their organizations

AI-3022 Training Objectives: Azure AI Search

• Create search solutions with Azure AI Search: Learn how to configure indexes, manage capacity, and apply filters, sorting, and scoring profiles to improve result relevance.
• Develop custom skills: Implement custom skills such as text classification or machine learning models to enrich data during the indexing process.
• Create a Knowledge Store: Define projections and store enriched data for later analysis.
• Apply advanced search features: Improve the search experience with semantic analysis, vector search, multiple languages, and geographic proximity sorting.
• Index external data: Use Azure Data Factory or the Azure AI Search API to index data from external sources.
• Maintain and optimize search solutions: Manage the security, performance, costs, and reliability of implemented solutions.
• Implement semantic and vector search: Configure semantic ranking and vector search to improve the accuracy and relevance of results.

Course Content AI-3022: Implementing Knowledge Mining with Azure AI Search

Module 1: Building an Azure AI Search Solution

• Introduction
• Capacity Management
• Understanding Search Components
• Understanding the Indexing Process
• Searching an Index
• Filtering and Sorting Data
• Improving the Index
• Exercise: Creating a Search Solution

Module 2: Creating a Custom Capability for Azure AI Search

• Introduction
• Defining the Custom Skill Schema
• Incorporating a Custom Skill
• Custom Text Classification Skill
• Custom Machine Learning Skill
• Exercise: Creating a Custom Skill for Azure AI Search

Module 3: Creating a Knowledge Store with Azure AI Search

• Introduction
• Defining Projections
• Defining a Knowledge Store
• Exercise: Creating a Knowledge Store

Module 4: Implementing Advanced Search Features in Azure AI Search

• Introduction
• Improving Document Ranking with Term Prioritization
• Improving Result Relevance by Adding Scoring Profiles
• Improving an Index by Using Analyzers and Tokenized Terms
• Improving an Index to Include Multiple Languages
• Improving the Search Experience by Sorting Results by Distance from a Given Landmark
• Exercise: Implementing Search Result Improvements

Module 5: Searching Data Outside the Azure Platform in Azure AI Search Using Azure Data Factory

• Introduction
• Indexing Data from External Data Sources Using Azure Data Factory
• Indexing Data Using the Azure AI Search Push API
• Exercise: Adding to an Index Using the Push API

Module 6: Maintaining an Azure AI Search Solution

• Introduction
• Managing the Security of an Azure AI Search Solution
• Optimizing the Performance of an Azure AI Search Solution
• Managing Costs of Azure AI Search Solutions
• Improving the Reliability of an Azure AI Search Solution
• Monitoring an Azure AI Search Solution
• Debugging Search Issues Using the Azure Portal
• Exercise: Troubleshooting Search Issues

Module 7: Reranking Searches with Semantic Ranking in Azure AI Search

• Introduction
• What is Semantic Ranking?
• Configuring Semantic Ranking
• Exercise: Using Semantic Ranking in an Index

Module 8: Performing Vector Search and Retrieval in Azure AI Search

• Introduction
• What is Vector Search?
• Preparing for Search
• Understanding Embeddings
• Exercise: Using the REST API to Execute Vector Search Queries

Prerequisites

• Familiarity with Microsoft Azure
• Experience developing applications with C# or Python

Language

• Course: English / Spanish

Microsoft Applied Skills

This course is part of the Microsoft Applied Skills Credentials.

Applied Skills: Explore all credentials in one guide