category
Build AI Apps With Azure Database For Postgresql
Type
Virtual
Classroom ILT
Skill Level
Available dates
Learning Path
Virtual
Duration
1 Day
TYPE
Virtual
Classroom ILT
LEARNING PATH
SKILL LEVEL
DURATION
AVAILABLE DATES
Introduction
Copilots are quickly becoming a popular way to use AI models, automate tasks, and improve productivity. In this learning path, you’ll explore options for adopting, using, and creating copilots. Generative AI, or GenAI, refers to a category of AI algorithms that can learn from existing data and generate new, original content based on natural language input.
The Azure AI extension is a powerful tool created by the PostgreSQL team at Microsoft that provides seamless integration between your Azure Database for PostgreSQL flexible server and Azure AI and Machine Learning services. It allows you to streamline the development process and harness the power of GenAI directly within their database, providing access to powerful natural language query processing capabilities. With just a few lines of SQL code, you can create vector embeddings, extract insights, and store them in the database. This extension bridges the data and actionable knowledge gap, making it an asset for enterprises building GenAI solutions.
Audience Profile
This course is designed for a range of professionals interested in advancing their skills in generative AI and AI project management. The primary audience includes:
- AI Enthusiasts: Individuals keen on exploring the fundamentals and applications of generative AI
- Data Scientists: Professionals looking to enhance their expertise in AI and machine learning
- IT Professionals: Those involved in developing or managing AI-driven solutions
- Business Analysts and Technical Leads: Individuals responsible for overseeing AI projects and integrating AI solutions into business processes
Pre-requisites
Familiarity with Azure and the Azure portal.
Course objectives
After completing this course, students will be able to:
- Grasp the fundamentals of generative AI and its role in AI development
- Get started with Microsoft Copilot Studio
- Introduction to Azure AI Studio and its core features
- Develop custom copilot solutions using Retrieval Augmented Generation (RAG) with your own data
Course content
| 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. | |
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| 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. | |
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| 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. | |
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| 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. | |
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| 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. | |
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| 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. | |
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| 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. | |
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Associated certifications and exam
There is no Associated certification & Exam for this course.
Microsoft Overview
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