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 create Azure AI Search solutions , implement custom skill sets, and add skill enrichment to an index to optimize search solutions.

Do you have locked information in structured and unstructured data sources? This course will teach you how to use Azure AI Search to extract key information from this data and enable 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

This course is geared towards:

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

Objectives of the AI-3022 training: 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 the relevance of results
• Develop custom skills : Implement custom skills such as text classification or machine learning models to enrich the data during the indexing process
• Create a Knowledge Store: Define projections and store enriched data for later analysis
• Apply advanced search features: Enhance the search experience with semantic analysis, vector search, multiple languages, and geographical proximity sorting.
• Index external data: Use Azure Data Factory or the Azure AI Search API to index data from external sources
• Maintaining and optimizing search solutions: Managing 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 the results

Course content AI-3022: Implementing knowledge mining with Azure AI Search

Module 1: Creating an Azure AI Search Solution

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

Module 2: Creating a custom capability for Azure AI Search

• Introduction
  
• Definition of the personalized skills scheme
  
• Incorporation of a personalized skill
  
• Custom text classification aptitude
  
• Customized Machine Learning Aptitude
  
• Exercise: Creating a custom skill for Azure AI Search

Module 3: Creating a knowledge store with Azure AI Search

• Introduction
  
• Definition of projections
  
• Definition of a knowledge store
  
• Exercise: Creating a knowledge repository

Module 4: Implement advanced search features in Azure AI Search

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

Module 5: Search for data outside the Azure platform in Azure AI Search using Azure Data Factory

• Introduction
  
• Indexing data from external data sources using Azure Data Factory
  
• Data indexing using the Azure AI Search insert API
  
• Exercise: Adding to an index using the insertion 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
  
• Azure AI Search solution cost management
  
• Improving the reliability of an Azure AI Search solution
  
• Monitoring an Azure AI Search solution
  
• Troubleshooting search issues using the Azure Portal
  
• Exercise: Solving search problems

Module 7: Perform a new classification of searches with semantic classification in Azure AI Search

• Introduction
  
• What is semantic classification?
  
• Semantic classification configuration
  
• 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 about insertion
  
• Exercise: Using the REST API to execute search vector queries

Prerequisites

To complete this training, it is recommended that:

• Be familiar with Microsoft Azure
• Do 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 will learn how to build AI applications with Azure Database for PostgreSQL.

Applied Skills: Explore all credentials in one guide