Overview
This learning path explores how the Azure AI and Azure Machine Learning Services integrations provided by the Azure AI extension for Azure Database for PostgreSQL – Flexible Server can enable you to build AI-powered apps.
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Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
- Get started with generative AI in Azure Database for PostgreSQL
- Enable semantic search in Azure Database for PostgreSQL
- Summarize data with Azure AI Services and Azure database for PostgreSQL
- Perform sentiment analysis and opinion mining with Azure Database for PostgreSQL
- Extract insights using Azure AI Language and Azure Database for PostgreSQL
- Translate Text with Azure AI Translator and Azure Database for PostgreSQL
- Use Azure Machine Learning for inferencing from Azure Database for PostgreSQL
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and a general understanding of AI and ML concepts.
Course Curriculum
Module 1: Get started with generative AI in Azure Database for PostgreSQL
This module explores how to effectively use Azure Database for PostgreSQL – Flexible Server and the Azure AI extension to develop powerful Generative AI applications.
Learning objectives
- Examine the concepts of generative AI and large language models and explore how they can be used to build rich AI applications.
- Evaluate the capabilities of azure_ai extension for PostgreSQL.
- Install and explore the azure_ai extension in an Azure Database for PostgreSQL – Flexible Server database.
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and a general understanding of Generative AI concepts.
Module 2: Enable semantic search in Azure Database for PostgreSQL
This module explores semantic search, embedding vectors, vector similarity, and using Azure Database for PostgreSQL – Flexible Server with PostgreSQL Vector and Azure AI extensions to generate, store, and query rows with semantic search.
Learning objectives
- Examine the concepts of semantic search & embedding vectors.
- Understand the difference between lexical search & semantic search.
- Evaluate the capabilities provided by the
azure_aiextension for PostgreSQL. - Install and explore the
vectorandazure_aiextensions in an Azure Database for PostgreSQL – Flexible Server database.
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and standard lexical search.
Module 3: Summarize data with Azure AI Services and Azure database for PostgreSQL
This module will examine how Azure AI Services and the Azure AI extension for Azure Database for PostgreSQL – Flexible Server enable Generative AI summarization techniques for condensing complex content into concise summaries.
Learning objectives
- Evaluate the summarization techniques available through Azure AI Services and the azure_ai extension.
- Examine the differences between extractive, abstractive, and query-focused summarization.
- Apply Generative AI summarization techniques to data in a PostgreSQL database to gain practical experience using Azure AI Services and the azure_ai extension for summarization.
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and a general understanding of Generative AI concepts.
Module 4: Perform sentiment analysis and opinion mining with Azure Database for PostgreSQL
This module will explore how Azure AI Services and the Azure AI extension for Azure Database for PostgreSQL – Flexible Server enable sentiment analysis and opinion mining to extract valuable insights from textual data.
Learning objectives
- Explore the fundamentals of sentiment analysis and how it can be leveraged to gain insights into user sentiments and emotions.
- Describe opinion mining techniques to identify sentiments related to specific attributes.
- Apply sentiment analysis to user reviews in a PostgreSQL database using the azure_ai extension.
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases, writing SQL queries, and a general understanding of Generative AI concepts.
Module 5: Extract insights using Azure AI Language and Azure Database for PostgreSQL
This module covers Azure AI Language tools including key phrase extraction, entity recognition, personally identifiable information recognition and redaction, and using Azure Database for PostgreSQL – Flexible Server with Azure AI to access these services.
Learning objectives
- Examine the concepts of key phrase extraction, entity recognition, and PII (personally identifiable information recognition) recognition.
- Understand the capabilities provided by the azure_ai extension for PostgreSQL to access Azure AI Language.
- Install the azure_ai extension in an Azure Database for PostgreSQL – Flexible Server database, and use it to extract key phrases, recognize entities in text, and redact PII.
Prerequisites
Before starting this module, you should have experience working with PostgreSQL databases and writing SQL queries.
Module 6: Translate Text with Azure AI Translator and Azure Database for PostgreSQL
This module guides you through translating textual data in Azure Database for PostgreSQL – Flexible Server using the Azure AI Translator service. You’ll load data into PostgreSQL and translate it into multiple languages.
Learning objectives
- Learn how to translate documents using the Azure AI Translator.
- Learn how the Azure AI extension enables seamless interaction between Azure Database for PostgreSQL – Flexible Server and Azure AI Translator.
- Understand one way to translate documents automatically as part of record insertion.
Prerequisites
- Before starting this module, you should have experience working with the PL/pgSQL language.
Module 7: Use Azure Machine Learning for inferencing from Azure Database for PostgreSQL
This module guides you through training and deploying an Azure Machine Learning model using automated machine learning. Then, you’ll call the model directly from Azure Database for PostgreSQL – Flexible Server.
Learning objectives
- Learn how to train and deploy an Azure Machine Learning model using automated machine learning.
- Learn how to invoke deployed Azure Machine Learning models from Azure Database for PostgreSQL – Flexible Server.
- Learn how to generate predictions for each row in an Azure Database for PostgreSQL – Flexible Server table.
Prerequisites
Before starting this module, you should have experience working with the PL/pgSQL language. Some knowledge of machine learning topics and Azure Machine Learning would be beneficial but not mandatory.
Dates & Locations
Exam & Certification
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