|
Implement knowledge mining with Azure AI Search (AI-3022)
Code:
AI-3022
Duration:
1 Day
|
$675
USD
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Using Azure AI Search, you can extract key insights from information locked up in structured and unstructured data sources, and enable applications to search and analyze them.
This course is available in the following formats:
Duration: 1 Day
Duration: 1 Day
| Call 800-798-3901 to enroll in this class! |
Module 1: Create an Azure AI Search solution
- Manage capacity
- Understand search components
- Understand the indexing process
- Search an index
- Apply filtering and sorting
- Enhance the index
Module 2: Create a custom skill for Azure AI Search
- Define the custom skill schema
- Add a custom skill
- Custom text classification skill
- Machine learning custom skill
Module 3: Create a knowledge store with Azure AI Search
- Define projections
- Define a knowledge store
Module 4: Implement advanced search features in Azure AI Search
- Improve the ranking of a document with term boosting
- Improve the relevance of results by adding scoring profiles
- Improve an index with analyzers and tokenized terms
- Enhance an index to include multiple languages
- Improve search experience by ordering results by distance from a given reference point
Module 5: Search data outside the Azure platform in Azure AI Search using Azure Data Factory
- Index data from external data sources using Azure Data Factory
- Index any data using the Azure AI Search push API
Module 6: Maintain an Azure AI Search solution
- Manage security of an Azure AI Search solution
- Optimize performance of an Azure AI Search solution
- Manage costs of an Azure AI Search solution
- Improve reliability of an Azure AI Search solution
- Monitor an Azure AI Search solution
- Debug search issues using the Azure portal
Module 7: Perform search reranking with semantic ranking in Azure AI Search
- What is semantic ranking
- Set up semantic ranking
Module 8: Perform vector search and retrieval in Azure AI Search
- What is vector search
- Prepare your search
- Understand embedding
Module 1: Create an Azure AI Search solution
- Manage capacity
- Understand search components
- Understand the indexing process
- Search an index
- Apply filtering and sorting
- Enhance the index
Module 2: Create a custom skill for Azure AI Search
- Define the custom skill schema
- Add a custom skill
- Custom text classification skill
- Machine learning custom skill
Module 3: Create a knowledge store with Azure AI Search
- Define projections
- Define a knowledge store
Module 4: Implement advanced search features in Azure AI Search
- Improve the ranking of a document with term boosting
- Improve the relevance of results by adding scoring profiles
- Improve an index with analyzers and tokenized terms
- Enhance an index to include multiple languages
- Improve search experience by ordering results by distance from a given reference point
Module 5: Search data outside the Azure platform in Azure AI Search using Azure Data Factory
- Index data from external data sources using Azure Data Factory
- Index any data using the Azure AI Search push API
Module 6: Maintain an Azure AI Search solution
- Manage security of an Azure AI Search solution
- Optimize performance of an Azure AI Search solution
- Manage costs of an Azure AI Search solution
- Improve reliability of an Azure AI Search solution
- Monitor an Azure AI Search solution
- Debug search issues using the Azure portal
Module 7: Perform search reranking with semantic ranking in Azure AI Search
- What is semantic ranking
- Set up semantic ranking
Module 8: Perform vector search and retrieval in Azure AI Search
- What is vector search
- Prepare your search
- Understand embedding