AI-3022: Implement knowledge mining with Azure AI Search

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

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

Do you have information locked in structured and unstructured data sources? In this course, you'll learn how, with Azure AI Search , you can extract key insights from this data and enable applications to search and analyze them.

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

Course aimed at

This course is aimed at:

• Artificial intelligence engineers
  
• Cloud solutions developers
  
• Data architects
  
• Business search professionals
  
• Data analysts who work with large volumes of unstructured data
  
• Technical teams looking to implement intelligent search solutions in their organizations

AI-3022: Azure AI Search Training Objectives

• Build search solutions with Azure AI Search: Learn how to configure indexes, manage capacity, and apply filters, sorts, and scoring profiles to improve the relevance of your results.
• 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 sorting by geographic proximity.
• 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 deployed 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: Creating an Azure AI Search Solution

• Introduction
• Capacity management
  
• Understanding Search Components
  
• Description of the indexing process
  
• Searching for an index
  
• Filtering and sorting data
  
• Index improvement
  
• Exercise: Creating a Search Solution

Module 2: Creating a Custom Capability for Azure AI Search

• Introduction
  
• Defining the custom skill set
  
• Adding a custom skill
  
• Custom text classification ability
  
• Custom Machine Learning Skill
  
• Exercise: Creating a custom skill for Azure AI Search

Module 3: Building a Knowledge Store with Azure AI Search

• Introduction
  
• Definition of projections
  
• Definition of a knowledge warehouse
  
• Exercise: Creating a knowledge warehouse

Module 4: Implement advanced search features in Azure AI Search

• Introduction
  
• Improve document classification with term prioritization
  
• Improving the relevance of results by adding scoring profiles
  
• Improving an index using analyzers and tokenized terms
  
• Improving an index to include multiple languages
  
• Improved search experience by sorting results by distance from a given landmark
  
• Exercise: Implementing improvements to search results

Module 5: Search 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 Insert API
  
• Exercise: Adding to an Index Using the Insert API

Module 6: Maintain an Azure AI Search Solution

• Introduction
  
• Managing the security of an Azure AI Search solution
  
• Optimizing the performance of an Azure AI Search solution
  
• Cost Management for 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: Search Problem Solving

Module 7: Reclassify searches with semantic classification in Azure AI Search

• Introduction
  
• What is semantic classification?
  
• Setting up semantic classification
  
• Exercise: Using semantic classification in an index

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

• Introduction
  
• What is vector search?
  
• Preparing for the search
  
• Information on insertion
  
• Exercise: Using the REST API to Execute Search Vector Queries

Prerequisites

To complete this training it is recommended:

• Be familiar with Microsoft Azure
• You have experience developing applications with C# or Python

Language

• Course: English / Spanish
  

Microsoft Applied Skills

This course is part of the Microsoft Applied Skills Credentials.

Earn this Microsoft Applied Skills credential. In this course, you'll learn how to build AI applications with Azure Database for PostgreSQL.

Applied Skills: Explore all credentials in one guide