Develop Natural Language Solutions in Azure (AI-3003)

Course 8783

  • Duration: 1 day
  • Language: English
  • Level: Intermediate

This course teaches learners how to develop natural language solutions using Microsoft Foundry and Azure AI services. Participants learn how to build applications and agents that analyze text, detect language, extract entities, identify personally identifiable information, transcribe and synthesize speech, and translate text and speech across languages.

Learners explore how Microsoft Foundry, Azure Language, Azure Speech, Translator, generative AI models, and Model Context Protocol servers can be used to create intelligent applications and agents that understand, process, and respond to natural language inputs.

Azure Natural Language Solutions Course Delivery Methods

  • In-Person

  • Online

  • Upskill your whole team by bringing Private Team Training to your facility.

Azure Natural Language Solutions Course Information

  • Course Benefits

    • Builds practical skills for developing natural language processing solutions in Azure
    • Helps learners create applications that analyze, interpret, and respond to text and speech
    • Covers both traditional Azure AI capabilities and newer agent-based patterns
    • Introduces Microsoft Foundry tools for text analysis, speech, translation, and voice-enabled AI experiences
    • Provides hands-on experience with APIs, SDKs, MCP servers, and AI agents
    • Supports developers and AI engineers building language-enabled applications and conversational AI solutions

    APIs: Application Programming Interfaces, which allow software applications to connect and exchange data. SDKs: Software Development Kits, which provide tools and code libraries developers use to build applications.MCP servers: Model Context Protocol servers, which allow AI agents to connect with external tools, systems, and data sources.

    Prerequisites

    Learners should have familiarity with Azure and the Azure portal, along with programming experience. Experience with Microsoft Foundry, generative AI model deployment, Python, REST APIs, webhooks, and cloud computing concepts is helpful for the more advanced agent and voice-focused sections.

    Exam Information

    None

Azure Natural Language Solutions Course Outline

Module 1 - Text Analysis with Azure Language

In this section, learners build foundational skills for analyzing text with Azure Language in Microsoft Foundry Tools. They learn how to detect language, recognize named entities, and identify personally identifiable information in text.

Lessons

Analyze text with Azure Language in Foundry Tools

  • Use Azure Language in Foundry Tools to extract semantic information from text
  • Detect the language used in text
  • Recognize named entities such as people, places, organizations, and other key terms
  • Extract personally identifiable information from text
  • Apply text analysis capabilities in an application scenario

 

Module 2 - Building Text Analysis Agents

In this section, learners explore how to build AI agents that use the Azure Language MCP server to perform text analysis tasks. They learn how MCP enables agents to discover and use tools dynamically and how to connect those tools to Microsoft Foundry agents.

Lessons

Develop a text analysis agent with the Azure Language MCP server

  • Describe the Azure Language MCP server and the text analysis capabilities it exposes
  • Explain how MCP enables dynamic tool discovery and selection by AI agents
  • Connect the Azure Language MCP server to an agent in Microsoft Foundry
  • Build a Python client application that invokes an agent for text analysis
  • Use the agent to perform tasks such as language detection, entity recognition, and personal information redaction

 

Module 3 - Speech-Capable Generative AI Applications

In this section, learners develop applications that can work with spoken language using speech-capable generative AI models in Microsoft Foundry. They learn how to deploy models, transcribe speech, and synthesize spoken responses.

Lessons

Develop a speech-capable generative AI application

  • Deploy speech-capable generative AI models in Microsoft Foundry
  • Select an appropriate model for speech-enabled scenarios
  • Use a generative AI model to transcribe speech
  • Use a generative AI model to synthesize speech
  • Build applications that support spoken input and output

 

Module 4 - Speech-Enabled Applications with Azure Speech

In this section, learners use Azure Speech in Microsoft Foundry Tools to build speech-enabled applications. They work with speech-to-text and text-to-speech APIs, configure audio settings and voices, and use Speech Synthesis Markup Language to control spoken output.

Lessons

Create speech-enabled apps with Azure Speech in Microsoft Foundry Tools

  • Use a Microsoft Foundry resource for Azure Speech
  • Implement speech recognition with the Speech to text API
  • Use the Text to speech API to implement speech synthesis
  • Configure audio formats and voices
  • Use Speech Synthesis Markup Language to customize synthesized speech

 

Module 5 - Building Speech Agents

In this section, learners build AI agents that use the Azure Speech MCP server to perform speech-to-text and text-to-speech tasks. They configure supporting Azure resources and create a client application that invokes the agent for speech scenarios.

Lessons

Develop a speech agent with the Azure Speech MCP server

  • Describe the Azure Speech MCP server and the speech capabilities it exposes
  • Explain how MCP enables dynamic tool discovery and selection by AI agents
  • Set up Azure Blob Storage for audio input and output
  • Connect the Azure Speech MCP server to an agent in Microsoft Foundry
  • Build a Python client application that invokes an agent to perform speech tasks

 

Module 6 - Voice Live Agents in Microsoft Foundry

In this section, learners develop conversational voice agents using the Azure Speech Voice Live platform. They explore the Voice Live API and SDK, then integrate Microsoft Foundry agents to create real-time voice-enabled AI experiences.

Lessons

Develop an Azure Speech Voice Live Agent in Microsoft Foundry

  • Describe the core components and capabilities of the Azure Speech Voice Live platform
  • Use the Voice Live API to create conversational AI solutions
  • Use the Voice Live SDK to build and deploy conversational AI solutions
  • Integrate Microsoft Foundry agents with the Voice Live API
  • Create voice agents that support real-time conversational experiences

 

Module 7 - Text and Speech Translation

In this section, learners use Translator and Azure Speech services in Microsoft Foundry Tools to build applications that translate text and speech between languages.

Lessons

Translate text and speech with Microsoft Foundry Tools

  • Identify options for translating text and speech in Microsoft Foundry Tools
  • Use Azure Translator in Foundry Tools to translate text
  • Use Azure Speech in Foundry Tools to translate speech
  • Build applications that support multilingual text and spoken interactions
  • Apply translation capabilities to real-world communication scenarios

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Azure Natural Language Solutions Course FAQs

This course is designed for AI engineers, developers, cloud developers, and technical professionals who want to build applications and agents that analyze text, process speech, and translate language using Microsoft Foundry and Azure AI services.

Yes. The Microsoft Learn path includes hands-on exercises for text analysis, speech-enabled applications, text analysis agents, speech agents, Voice Live agents, and translation scenarios.

Yes. Learners should have programming experience and familiarity with Azure. Python experience is especially helpful for the MCP server and agent development sections.

The course covers Microsoft Foundry, Foundry Tools, Azure Language, Azure Speech, Azure Translator, generative AI models, Speech to text, Text to speech, Voice Live API, Voice Live SDK, Model Context Protocol, and Python client applications.

Microsoft lists an Achievement Code for this learning path, but no dedicated certification exam is listed directly on the learning path page.