Cognitive & Knowledge Services

Microsoft via Coursera

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Overview

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Design enterprise solutions using Azure's full suite of specialized cognitive AI services across natural language processing, computer vision, document intelligence, speech, and knowledge mining. You will evaluate NLP solution approaches using Azure AI Language and Translator, assess document and image intelligence strategies with Azure AI Vision and Document Intelligence, compare architectural considerations for speech-enabled solutions, and apply decision frameworks to knowledge mining using Azure AI Search enrichment pipelines and semantic ranking. This course is designed specifically for AI architects and engineers building customer-facing, analytics-heavy, or document-intensive solutions who have completed foundational Azure AI platform training and are ready to operationalize specialized cognitive services. While prior exposure to Azure AI Language, Vision, or Speech is helpful, it is not required. Completing this course equips you with the decision frameworks required to deploy purpose-built, enterprise-grade AI solutions across complex business scenarios.

Syllabus

NLP Solutions: Design with Azure AI Language Services
- This module develops the skill of mapping enterprise text analysis and conversational AI requirements to the correct Azure AI Language capabilities—moving beyond treating Azure AI Language as a single service to be applied uniformly, and toward a structured selection framework that matches each capability to the specific task type it is optimized for. Learners design the service configuration and integration architecture that delivers those capabilities in a production solution.
NLP Solutions: Architect Multilingual Solutions & Design Service Combinations
- This module develops the skill of designing multilingual NLP architectures—integrating Azure AI Translator with Azure AI Language capabilities to support global use cases—and the broader service combination design skill of selecting and assembling the right combination of capabilities for a given conversational AI or text analytics scenario. Learners extend the single-language architecture designed in Module 1 to a full five-language multilingual deployment.
Vision Solutions: Design with Azure AI Vision
- This module develops the skill of designing Azure AI Vision solutions—selecting the appropriate prebuilt capabilities for image analysis, real-time video analytics, physical space monitoring, and video processing scenarios, and determining when custom vision model training is required—using a structured decision framework that prevents both over-engineering with unnecessary custom training and under-engineering with insufficient prebuilt capabilities.
Document Intelligence Solutions: Design with Azure AI Document Intelligence
- This module develops the skill of designing enterprise document-processing solutions using Azure AI Document Intelligence—selecting appropriate prebuilt extraction models for standard document types, designing custom model-training pipelines for non-standard forms, and integrating extracted structured data into downstream systems. The insurance claims processing architecture is completed by connecting the document intelligence pipeline to the vision architecture from Module 1.
Speech Solutions: Design STT, TTS & Custom Speech Models
- This module develops the skill of designing complete Azure AI Speech solution architectures—selecting between real-time and batch transcription using a structured decision framework, configuring custom speech models for domain-specific vocabulary, and designing text-to-speech solutions—with latency, scalability, and cost as the primary architectural constraints.
Speech Solutions: Architect Multimodal Solutions with Speech, Language & Vision
- This module develops the skill of architecting multimodal solutions that integrate Azure AI Speech with Azure AI Language and Vision capabilities within a Microsoft Foundry project, designing the service integration architecture, orchestration pattern, and unified output design that produces a coherent multimodal solution from services that were designed independently.
Knowledge Mining: Design Indexer Pipelines, Enrichment Skillsets & Knowledge Stores
- This module develops the skill of designing the knowledge mining ingestion and enrichment architecture—configuring Azure AI Search indexers for large-scale document archives, designing AI enrichment skillsets that apply built-in and custom cognitive skills to extract structured insights from unstructured content, and designing knowledge stores for downstream analytics consumption. You will work through the complete ingestion architecture for a 500,000-document legal firm archive, making the built-in vs. custom skill decision for each enrichment requirement.
Knowledge Mining: Architect Intelligent Search Experiences with Semantic Ranking & Hybrid Search
- This module develops the skill of designing the query and retrieval architecture that makes an enriched knowledge base useful for intelligent search, including configuring semantic ranking, hybrid search pipelines, and custom scoring profiles to optimize relevance for the specific query patterns and content characteristics of a large legal document archive. Learners will explore the interaction between scoring profiles and the semantic ranker, including default behavior where scoring profiles shape initial L1 candidate selection before semantic scores re-rank the final results.
Project Module: Cognitive & Knowledge Services Architecture
- Learners produce a portfolio-ready Cognitive and Knowledge Services Architecture document for a provided enterprise scenario—integrating NLP, document intelligence, vision, speech, and knowledge mining capabilities into a unified multi-service solution architecture that mirrors the deliverable a practicing Azure AI Solutions Architect would produce for a complex, multi-capability enterprise AI engagement.