Microsoft Azure AI-102

About this course

This advanced certification course is designed for AI engineers, data scientists, and developers who build, manage, and deploy AI solutions on Microsoft Azure. Learners will master the end-to-end lifecycle of Azure AI services, including Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services. The curriculum covers designing and implementing AI solutions for vision, natural language processing (NLP), speech, decision support, and generative AI using Azure OpenAI Service. Participants will gain hands-on experience with REST APIs, SDKs, container deployment, and responsible AI principles to ensure ethical and compliant solutions. Key topics include deploying models, monitoring performance, integrating AI with existing applications, and automating AI pipelines using Azure DevOps. Emphasis is placed on securing AI workloads, managing identity with Azure Active Directory, and optimizing cost and performance for production-scale deployments. By the end of this course, learners will be prepared to pass the AI-102 certification exam and implement enterprise-ready AI solutions.

Course Outline

Module 01: Plan and manage an Azure AI solution

Select the appropriate Azure AI services

Select the appropriate service for a generative AI solution

Select the appropriate service for a computer vision solution

Select the appropriate service for a natural language processing solution

Select the appropriate service for a speech solution

Select the appropriate service for an information extraction solution

Select the appropriate service for a knowledge mining solution

Plan, create and deploy an Azure AI service

Plan for a solution that meets Responsible AI principles

Create an Azure AI resource

Choose the appropriate AI models for your solution

Deploy AI models using the appropriate deployment options

Install and utilize the appropriate SDKs and APIs

Determine a default endpoint for a service

Integrate Azure AI services into a CI/CD pipeline

Plan and implement a container deployment

Manage, monitor, and secure an Azure AI service

Monitor an Azure AI resource

Manage costs for Azure AI services

Manage and protect account keys

Manage authentication for an Azure AI Service resource

Implement AI solutions responsibly

Implement content moderation solutions

Configure responsible AI insights, including content safety

Implement responsible AI, including content filters and blocklists

Prevent harmful behavior, including prompt shields and harm detection

Design a responsible AI governance framework

Module 02: Implement generative AI solutions

Build generative AI solutions with Azure AI Foundry

Plan and prepare for a generative AI solution

Deploy a hub, project, and necessary resources with Azure AI Foundry

Deploy the appropriate generative AI model for your use case

Implement a prompt flow solution

Implement a RAG pattern by grounding a model in your data

Evaluate models and flows

Integrate your project into an application with Azure AI Foundry SDK

Utilize prompt templates in your generative AI solution

Use Azure OpenAI Service to generate content

Provision an Azure OpenAI Service resource

Select and deploy an Azure OpenAI model

Submit prompts to generate code and natural language responses

Use the DALL-E model to generate images

Integrate Azure OpenAI into your own application

Use large multimodal models in Azure OpenAI

Implement an Azure OpenAI Assistant

Optimize and operationalize a generative AI solution

Configure parameters to control generative behavior

Configure model monitoring and diagnostic settings, including performance and resource consumption

Optimize and manage resources for deployment, including scalability and foundational model updates

Enable tracing and collect feedback

Implement model reflection

Deploy containers for use on local and edge devices

Implement orchestration of multiple generative AI models

Apply prompt engineering techniques to improve responses

Fine-tune a generative model

Module 03: Implement an agentic solution

Create custom agents

Understand the role and use cases of an agent

Configure the necessary resources to build an agent

Create an agent with the Azure AI Agent Service

Implement complex agents with Semantic Kernel and Autogen

Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities

Test, optimize and deploy an agent

Module 04: Implement computer vision solutions

Analyze images

Select visual features to meet image processing requirements

Detect objects in images and generate image tags

Include image analysis features in an image processing request

Interpret image processing responses

Extract text from images using Azure AI Vision

Convert handwritten text using Azure AI Vision

Implement custom vision models

Choose between image classification and object detection models

Label images

Train a custom image model, including image classification and object detection

Evaluate custom vision model metrics

Publish a custom vision model

Consume a custom vision model

Build a custom vision model code first

Analyze videos

Use Azure AI Video Indexer to extract insights from a video or live stream

Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video

Module 05: Implement natural language processing solutions

Analyze and translate text

Extract key phrases and entities

Determine sentiment of text

Detect the language used in text

Detect personally identifiable information (PII) in text

Translate text and documents by using the Azure AI Translator service

Process and translate speech

Integrate generative AI speaking capabilities in an application

Implement text-to-speech and speech-to-text using Azure AI Speech

Improve text-to-speech by using Speech Synthesis Markup Language (SSML)

Implement custom speech solutions with Azure AI Speech

Implement intent and keyword recognition with Azure AI Speech

Translate speech-to-speech and speech-to-text by using the Azure AI Speech service

Implement custom language models

Create intents, entities, and add utterances

Train, evaluate, deploy, and test a language understanding model

Optimize, backup, and recover language understanding model

Consume a language model from a client application

Create a custom question answering project

Add question-and-answer pairs and import sources for question answering

Train, test, and publish a knowledge base

Create a multi-turn conversation

Add alternate phrasing and chit-chat to a knowledge base

Export a knowledge base

Create a multi-language question answering solution

Implement custom translation, including training, improving, and publishing a custom model

Module 06: Implement knowledge mining and information extraction solutions

Implement an Azure AI Search solution

Provision an Azure AI Search resource, create an index, and define a skillset

Create data sources and indexers

Implement custom skills and include them in a skillset

Create and run an indexer

Query an index, including syntax, sorting, filtering, and wildcards

Manage Knowledge Store projections, including file, object, and table projections

Implement semantic and vector store solutions

Implement an Azure AI Document Intelligence solution

Provision a Document Intelligence resource

Use prebuilt models to extract data from documents

Implement a custom document intelligence model

Train, test, and publish a custom document intelligence model

Create a composed document intelligence model

Extract information with Azure AI Content Understanding

Create an OCR pipeline to extract text from images and documents

Summarize, classify, and detect attributes of documents

Extract entities, tables, and images from documents

Process and ingest documents, images, videos, and audio with Azure AI Content Understanding

$ 150

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Duration

30hrs

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Module

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