AI-3019 Build AI apps with Azure Database for PostgreSQL

Price
Net: €
VAT: €

Price
Price on Request

Duration
1 day

For companies and job seekers:
this course is 100% fundable!
 

Location

Course Language
English

Training Solutions
Online Live

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Modern AI applications arise where intelligent models and powerful databases interact. The focus is on practical development, stable architecture, and efficient data processing in cloud environments.

Key topics

  • Integration of AI applications with Azure Database for PostgreSQL.
  • Data modeling and query optimization for AI workloads.
  • Use of cloud-native features for scaling and performance.
  • Security, governance, and reliable operation of databases.
  • Interaction between application logic, data, and AI services.

Prerequisites
Basic knowledge of databases, SQL, and cloud concepts, as well as initial experience with application development and Azure environments.

Target audience
Software developers, data professionals, cloud engineers, and technical roles who want to implement or expand AI-powered applications with relational databases.

The focus is on stable architectures, clean databases, and modern cloud technology as the foundation for scalable AI solutions with long-term relevance.
 

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course content
  • Overview of generative AI models
  • Features of the Azure AI extension
  • Understanding the structure of Azure OpenAI
  • Review Azure Cognitive architecture
  • Analyze Azure ML structure
  • Understanding semantic search
  • Saving vectors
  • Creating embeddings
  • Learn about practical examples
  • Explain extraction
  • Examine abstraction
  • Rate mood
  • Record opinions
  • Filter out keywords
  • Identify units
  • Recognize personal data
  • Azure AI Translator
  • Translate data in PostgreSQL
  • Try Azure ML
  • Use models in Azure

Frequently Asked Questions

  • Company-specific Copilots provide answers based on a company’s own data and processes. This training course demonstrates how to use Azure AI Studio to develop powerful AI assistants for various use cases.
  • Continuing education supports the entire development process—from planning and integrating knowledge sources to testing, optimizing, and deploying your own copilots.
  • One key focus is on integrating the company's own documents and data sources. This enables Copilots to draw on information from the corporate context and provide accurate answers.
  • The quality of an AI assistant depends largely on how the prompts are designed. This training course teaches methods for developing prompts specifically designed to produce reliable and transparent results.
  • Topics covered include the development of generative AI applications, the use of Azure AI Studio, model configuration, and the testing and evaluation of Copilots.
  • This training course demonstrates how copilots are tested, optimized, and evaluated for safety, quality, and response behavior before they are deployed in day-to-day business operations.
  • Real-world scenarios from customer service, knowledge management, internal assistance systems, and the automation of information processes illustrate the use of modern AI copilots.
  • The content focuses on generative AI, large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and the development of intelligent copilot applications using Azure AI Studio.
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Do you have any further questions? Please contact us.