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AI-3003 Build a natural language processing solution with Azure AI Services

Natural language processing is becoming a core component of data-driven systems. With Azure AI, text can be structured, understood and evaluated – for scalable applications in the cloud.

Key topics

  • Using and configuring Azure AI Services for language processing tasks.
  • Analysing text, extracting linguistic meaning and evaluating semantic content.
  • Applying NLP models and AI APIs to real-world use cases.
  • Embedding NLP solutions into existing Azure infrastructures and scaling them.
  • Considering data protection and security aspects when deploying AI.

Prerequisite
Those with initial experience in cloud services and Azure will find it easy to get started. Knowledge of APIs and a basic understanding of data structures is advantageous.

Target group
For professionals working in cloud-native projects who want to apply language models in practice – especially from development, data engineering and AI-related roles.

This course gives you the technical foundation to implement NLP features in Azure projects independently and adapt them sustainably.

course content
  • Set up and provision an Azure AI language resource
  • Automatically identify languages
  • Extract key terms from texts
  • Detect and evaluate sentiment in texts
  • Isolate entities from text content
  • Identify links between entities
  • Learn the fundamentals of automated question answering
  • Understand the differences between Question Answering and Azure AI Language
  • Build your own knowledge base
  • Implement multi-turn conversational scenarios
  • Test and publish the knowledge base
  • Use the knowledge base actively in applications
  • Systematically improve answer quality
  • Use predefined capabilities of Azure AI Language Service
  • Plan and build infrastructure for conversational language understanding
  • Define intents, utterances and entities clearly
  • Use patterns to classify similar speech expressions
  • Integrate prebuilt entity components into models
  • Train, evaluate, publish and monitor CLU models
  • Distinguish between different types of text classification projects
  • Plan and implement custom classification projects
  • Understand the concepts of custom named entity recognition
  • Annotate and label training data precisely
  • Train a recognition model and evaluate its quality
  • Set up and configure Azure AI Translator resources
  • Clarify differences between language detection, translation and transliteration
  • Fine-tune translation parameters
  • Create user-specific translation rules
  • Provision and manage Azure speech resources
  • Transcribe speech with the Azure AI Speech-to-Text API
  • Output text as speech using the Text-to-Speech API
  • Configure audio formats and voice properties
  • Use SSML for advanced speech output
  • Configure and activate an Azure resource for speech translation
  • Convert spoken language into translated text
  • Synthesise translated content as speech output

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