Artificial intelligence has transitioned from being a specialized area of research to a mainstream component of modern software development. Businesses and developers are increasingly embedding AI features into applications to enhance user experiences, automate decision-making, and generate deeper insights from data. Microsoft Azure provides a comprehensive suite of AI services that support this transformation, and the AI-102 course has been designed specifically to equip developers with the skills to implement these capabilities effectively.

This section introduces the AI-102 course, outlines its target audience, specifies the technical prerequisites needed for success, and explains the instructional methods used throughout the training.

Introduction to the AI-102 Course

AI-102, officially titled Designing and Implementing an Azure AI Solution, is a four-day, instructor-led course tailored for software developers aiming to create AI-enabled applications using Azure’s cognitive services and related tools. The course provides comprehensive coverage of Azure Cognitive Services, Azure Cognitive Search, and the Microsoft Bot Framework. These platforms enable developers to implement functionality such as language understanding, text analytics, speech recognition, image processing, face detection, and intelligent search into their applications.

The course is hands-on and highly interactive. Students learn to work with these services using programming languages such as C# or Python, while also becoming comfortable with REST-based APIs and JSON. Emphasis is placed not just on building AI features, but also on securing, deploying, and maintaining those capabilities at scale.

By the end of the course, participants will be well-positioned to design, develop, and manage intelligent cloud-based solutions using Microsoft Azure’s AI offerings. This makes the course a core component of the learning journey for developers pursuing the Azure AI Engineer Associate certification.

Intended Audience

AI-102 is targeted at software engineers and developers who are currently building or are planning to build AI-driven applications on the Azure platform. These individuals typically have some experience with cloud computing and are proficient in either C# or Python.

The ideal course participants include:

• Software developers building intelligent enterprise or consumer applications
  
• Engineers involved in machine learning and AI model integration
  
• Developers creating conversational bots or search-based applications
  
• Cloud solution architects and consultants focused on Azure AI.
  
• Technical professionals working with APIs and cognitive computing
  

Participants are expected to have familiarity with REST-based services and a desire to deepen their understanding of how AI services can be used programmatically within larger application ecosystems.

Whether building real-time speech translation tools, chatbots, recommendation engines, or document analysis systems, professionals attending this course will learn how to approach these tasks with a solid architectural and implementation strategy.

Prerequisites for Attending the Course

While the course is designed for developers, it assumes that participants bring a certain level of technical proficiency and familiarity with programming and cloud technologies. These prerequisites ensure that learners can engage effectively with both the theoretical and hands-on components of the training.

Participants should meet the following prerequisites:

• A general understanding of Microsoft Azure, including experience navigating the Azure portal
  
• Practical programming experience with either C# or Python
  
• Familiarity with JSON formatting and REST-based API interaction
  
• Basic knowledge of HTTP methods such as GET, POST, PUT, and DELETE
  

Those who do not yet have experience with C# or Python are encouraged to complete a basic programming path, such as “Take your first steps with C#” or “Take your first steps with Python,” before attending the course. These preliminary tracks introduce programming fundamentals and syntax required for AI-102.

For individuals who are new to artificial intelligence, a broader foundational understanding of AI principles can also be helpful. Completing the Azure AI Fundamentals certification before AI-102 is recommended for learners who want to gain confidence in the core concepts of artificial intelligence before diving into hands-on development.

Course Delivery and Methodology

The AI-102 course follows a practical, instructor-led format conducted over four days. It combines lectures with interactive labs and real-world scenarios, ensuring that students gain hands-on experience while also building a solid conceptual framework.

The instructional methodology includes:

• Instructor-led sessions: In-depth lectures introduce each topic, supported by visual diagrams, demonstrations, and walkthroughs.
  
• PowerPoint presentations: Structured slides are used to reinforce key concepts, define architecture, and highlight integration patterns.
  
• Hands-on labs: Each module includes practical labs where students use Azure services directly to build and test AI-powered solutions.
  
• Live coding demonstrations: Instructors often demonstrate real-time coding practices to show how specific services are implemented.
  
• Discussions and problem-solving: Students are encouraged to engage in group discussions, analyze use cases, and share implementation ideas.
  
• Q&A and interactive feedback: Throughout the course, learners can ask questions and receive guidance, making the learning process more dynamic and adaptive to individual needs.
  

This mix of theory and hands-on activity ensures that developers leave the course not only understanding how Azure AI services work but also feeling confident in their ability to use them in production-grade applications.

Learning Outcomes and Objectives

The AI-102 course has been structured to help learners achieve a broad range of technical objectives, reflecting the types of tasks AI engineers face in modern software environments. Upon completion of the course, students will be able to:

• Understand core considerations in building AI-enabled applications
  
• Create and configure Azure Cognitive Services instances for various AI workloads.
  
• Secure AI services using authentication and access control models
  
• Build applications that analyze and interpret natural language text.
  
• Develop speech recognition and synthesis capabilities
  
• Translate text and speech between different languages.
  
• Implement natural language understanding through prebuilt and custom models
  
• Use QnA Maker to create and manage knowledge bases for conversational AI
  
• Develop chatbots using the Microsoft Bot Framework SDK and Composer.
  
• Use computer vision APIs to analyze, tag, and describe images.
  
• Train and deploy custom vision models for specific object detection scenarios.
  
• Detect, identify, and analyze human faces in images and video
  
• Extract text from images and scanned documents using OCR capabilities
  
• Apply AI to large-scale content through intelligent search and knowledge mining.
  

These outcomes reflect the diversity of AI use cases and give learners the flexibility to apply what they’ve learned across a wide range of industries and application types.

This part of the breakdown has provided a full overview of the AI-102 course, beginning with its scope and purpose, identifying the intended audience, and outlining the technical prerequisites for successful participation. It also described the course’s delivery format and instructional strategy and presented the detailed learning outcomes that students can expect to achieve by the end of the training.

In the next part, the focus will shift to the detailed structure of the course modules. We will explore how the course progresses through topics like cognitive services, natural language processing, speech applications, and more. Each module’s lessons, labs, and key takeaways will be presented clearly to show how the course builds a complete AI development skillset using Microsoft Azure.

Course Modules – Azure AI, Cognitive Services, and Natural Language Processing

The AI-102 course is structured into a series of well-defined modules. Each module focuses on a specific set of Azure AI capabilities, gradually expanding from foundational concepts to more complex implementations. The approach is incremental, combining lessons with practical lab exercises to reinforce learning through hands-on application.

This part of the breakdown covers the first group of modules that form the core of Azure-based AI development. These include an introduction to artificial intelligence on Azure, cognitive services setup and management, and natural language processing using text analytics and translation.

Module 1: Introduction to AI on Azure

The course begins by setting the stage with a high-level overview of artificial intelligence and how Microsoft Azure supports the development and deployment of AI solutions.

Lessons

• Introduction to Artificial Intelligence
  
• Artificial Intelligence in Azure
  

This module introduces the fundamental types of AI workloads, including vision, speech, language, and decision-making. It explains the difference between pre-trained models and custom models, and it positions Azure Cognitive Services as a gateway to enterprise AI without the need for building and training models from scratch.

Learners also get familiar with the broader Azure ecosystem as it relates to AI, including the use of containers, REST APIs, SDKs, and cloud infrastructure needed to deploy AI solutions at scale.

Learning Outcomes

By the end of this module, students will be able to:

• Describe common AI application patterns and use cases
  
• Identify key Azure services that support AI-enabled applications
  
• Understand the role of Cognitive Services in enterprise development.
  

This module is foundational, giving learners a conceptual map of what lies ahead and how to align technical goals with Azure’s AI capabilities.

Module 2: Developing AI Apps with Cognitive Services

Once the AI concepts are introduced, the next step is to dive into Azure Cognitive Services, which form the backbone of many AI workloads on Azure. This module focuses on provisioning, managing, and securing these services.

Lessons

• Getting Started with Cognitive Services
  
• Using Cognitive Services for Enterprise Applications
  

This module guides learners through the process of creating Cognitive Services accounts and managing them in the Azure portal. It emphasizes best practices for configuring keys, endpoints, and security access.

Labs

• Get Started with Cognitive Services
  
• Manage Cognitive Services Security
  
• Monitor Cognitive Services
  
• Use a Cognitive Services Container
  

The labs in this module offer practical experience in deploying AI services and working with their configurations. Students also learn how to deploy services in containers for flexible and portable use in isolated or on-premises environments.

Learning Outcomes

By the end of this module, students will be able to:

• Provision and configure Azure Cognitive Services for different workloads
  
• Secure access using authentication keys and network restrictions
  
• Monitor usage and performance through Azure metrics and logging tools.
  
• Deploy Cognitive Services as containers for local or hybrid environments.
  

This module establishes the operational skills required to prepare Cognitive Services for integration into applications.

Module 3: Getting Started with Natural Language Processing

Natural Language Processing (NLP) allows applications to understand, interpret, and generate human language. This module focuses on Azure’s prebuilt language services that enable developers to work with text and translation.

Lessons

• Analyzing Text
  
• Translating Text
  

Students are introduced to the Text Analytics API, which provides features like sentiment analysis, key phrase extraction, language detection, and entity recognition. The module also introduces the Translator service, which supports multi-language translation using pre-trained models.

Labs

• Analyze Text
  
• Translate Text
  

The lab exercises allow students to build basic applications that analyze text content, detect the language, extract insights, and translate input from one language to another using the Translator API.

Learning Outcomes

By the end of this module, students will be able to:

• Use Text Analytics to perform language detection and sentiment analysis
  
• Extract key phrases and named entities from unstructured text.
  
• Translate text between languages using Azure Translator
  
• Combine language services to enhance application functionality.
  

This module helps learners understand how language services can be embedded into applications that need to interact with users through textual inputs, such as reviews, emails, or social media content.

Module 4: Building Speech-Enabled Applications

Speech services are crucial for applications that require hands-free operation, accessibility features, or real-time voice interaction. This module explores the capabilities of Azure’s Speech service for both speech-to-text and text-to-speech functionality.

Lessons

• Speech Recognition and Synthesis
  
• Speech Translation
  

Learners gain experience using the Speech SDK and APIs to convert spoken language into text, as well as to synthesize spoken output from text. The speech translation capability allows real-time translation between multiple languages, useful for international communication applications.

Labs

• Recognize and Synthesize Speech
  
• Translate Speech
  

The labs provide direct experience working with microphone input, speech recognition models, and audio playback features. They also allow learners to implement translation scenarios where users can speak in one language and receive a response in another.

Learning Outcomes

By the end of this module, students will be able to:

• Convert speech to text using the Azure Speech service
  
• Convert text to speech and configure voice styles and tones.
  
• Translate spoken content between different languages
  
• Build applications that interact with users via voice interfaces
  

This module is especially relevant for building voice assistants, automated customer service systems, and accessibility tools.

Module 5: Creating Language Understanding Solutions

Language Understanding (LUIS) is a critical part of building conversational and intent-driven applications. This module introduces the Language Understanding service and its integration with speech and chat applications.

Lessons

• Creating a Language Understanding App
  
• Publishing and Using a Language Understanding App
  
• Using Language Understanding with Speech
  

The module teaches students how to train a custom language model that can identify user intent and extract relevant information (entities) from input text. It also covers how to deploy these models and integrate them into applications.

Labs

• Create a Language Understanding App
  
• Create a Language Understanding Client Application
  
• Use the Speech and Language Understanding Services
  

Labs guide participants through creating intents and entities, training the model, and using it from client applications, including voice-based clients.

Learning Outcomes

By the end of this module, students will be able to:

• Design and configure custom Language Understanding applications
  
• Train and evaluate intent recognition models
  
• Build applications that interact with Language Understanding via REST APIs
  
• Combine Language Understanding with speech recognition for voice-based systems.
  

This module bridges the gap between static text analysis and dynamic conversational systems by teaching how to handle user input with context and nuance.

This part has covered the first set of technical modules in the AI-102 course. Starting with a foundational understanding of artificial intelligence and Azure’s role in delivering AI services, it progresses into the practical deployment and consumption of Azure Cognitive Services. Learners explore text analytics, language translation, speech recognition, and language understanding, with each topic reinforced through hands-on labs and real-world scenarios.

These modules lay the groundwork for more advanced AI development tasks, such as question-answering systems, chatbots, computer vision, and intelligent search, which will be covered in the next section.

Question Answering, Conversational AI, and Computer Vision in Azure

As modern applications evolve, the expectation is for software to not only process data but also to communicate naturally, answer user queries, and interpret visual input. In this part, we explore how Azure equips developers with the tools to build advanced AI-driven systems for question answering, conversational bots, and computer vision.

These modules guide learners through implementing user-friendly interfaces and building systems that can understand spoken and written inputs and analyze visual content like images and videos. The services covered in this part play a key role in creating smart, intuitive, and accessible software applications.

Module 6: Building a QnA Solution

This module introduces the concept of Question and answering systems using Azure’s QnA Maker. It enables developers to transform unstructured documents into searchable, natural-language-based responses.

Lessons

• Creating a QnA Knowledge Base
  
• Publishing and Using a QnA Knowledge Base
  

Students are taught how to extract questions and answers from documents like product manuals, FAQs, and support articles. The QnA Maker service enables the creation of a structured knowledge base that can be queried using natural language inputs.

Labs

• Create a QnA Solution
  

In this lab, learners create a knowledge base from a sample document, test it using the built-in QnA Maker tools, and integrate it into a simple application to provide user-facing responses.

Learning Outcomes

By the end of this module, learners will be able to:

• Create and configure a knowledge base using QnA Maker
  
• Train and publish the knowledge base
  
• Query the knowledge base through a web interface or a bot
  
• Improve user experiences by enabling accurate, document-based answers.
  

QnA Maker is especially useful in support applications, virtual assistants, and helpdesk automation, where quick and reliable information retrieval is necessary.

Module 7: Conversational AI and the Azure Bot Service

Building intelligent bots capable of maintaining conversations is a key application of Azure AI. This module provides an introduction to creating chatbots using the Microsoft Bot Framework SDK and Bot Framework Composer.

Lessons

• Bot Basics
  
• Implementing a Conversational Bot
  

The lesson covers the fundamental components of a bot application, including dialog flow, message handling, channel integration, and state management. Students learn how to design conversation experiences using both code (Bot Framework SDK) and low-code tools (Bot Framework Composer).

Labs

• Create a Bot with the Bot Framework SDK
  
• Create a Bot with Bot Framework Composer
  

The lab work allows learners to create a basic chatbot using both approaches. They test the bot’s ability to interpret user input, return responses, and integrate with external services like Language Understanding and QnA Maker.

Learning Outcomes

By the end of this module, students will be able to:

• Develop conversational bots using the Bot Framework SDK
  
• Design conversation flows and dialogs using Bot Framework Composer
  
• Integrate bots with other Azure services like QnA Maker and Language Understanding
  
• Deploy bots across communication platforms such as Teams, Web Chat, and others.
  

Bots play a growing role in customer service, onboarding, education, and virtual assistance. This module equips developers with the tools needed to deliver these capabilities in scalable, flexible ways.

Module 8: Getting Started with Computer Vision

Computer Vision enables applications to interpret and analyze visual input such as images and video. This module introduces Azure’s prebuilt computer vision capabilities.

Lessons

This module teaches how to use Azure’s Computer Vision API to extract meaningful data from images. Key features include object detection, image classification, text extraction (OCR), and image tagging.

Students learn how to call the Computer Vision API using REST endpoints or SDKs and retrieve structured information about the content of an image.

Labs

• Use the Computer Vision API to analyze images.
  
• Tag, describe, and categorize content
  

These labs offer hands-on experience in submitting images to the API and retrieving responses that include object names, confidence scores, and image descriptions.

Learning Outcomes

By the end of this module, students will be able to:

• Analyze images using pre-trained computer vision models
  
• Identify objects, text, and metadata in photographs or screenshots.
  
• Describe visual content using natural language tags.
  
• Create applications that automatically process and classify images
  

This module lays the foundation for adding AI-driven visual analysis to applications, which can be used in areas such as digital asset management, accessibility features, surveillance systems, and document automation.

Module 9: Developing Custom Vision Solutions

While prebuilt models work well for general tasks, sometimes applications require domain-specific image recognition. This module teaches students how to build and deploy custom vision models tailored to unique needs.

Lessons

• Collecting and labeling data
  
• Training and evaluating models
  
• Deploying custom models to endpoints
  

Students are guided through using Azure Custom Vision, a service that lets developers upload labeled image datasets, train a model to recognize specific objects or categories, and evaluate its performance using test images.

Labs

• Train a custom vision model
  
• Test and deploy the model for real-time predictions
  

The labs show learners how to create their own classification or object detection models, making decisions about data quality, labeling strategy, and model optimization.

Learning Outcomes

By the end of this module, students will be able to:

• Design and train custom image classification models
  
• Label image data and manage datasets
  
• Evaluate model accuracy and iterate on training.
  
• Deploy models to Azure or to edge devices using containers
  

This module is vital for applications in retail (product identification), healthcare (diagnostic imaging), manufacturing (quality inspection), and agriculture (crop monitoring), where general-purpose models fall short.

Module 10: Detecting, Analyzing, and Recognizing Faces

Facial recognition adds another dimension to computer vision, enabling applications to identify or verify individuals in images or live video.

Lessons

• Face detection
  
• Face verification and identification
  
• Emotion and attribute analysis
  

This module introduces the Azure Face API, which can detect human faces, match them against known identities, and extract attributes such as age, emotion, or glasses.

Labs

• Use Face API for detection and identification
  
• Analyze facial attributes from images.
  

The labs allow learners to create a sample application that identifies users, groups them, and provides data about their expressions or characteristics.

Learning Outcomes

By the end of this module, students will be able to:

• Detect faces and draw bounding boxes on images
  
• Match detected faces to known identities for verification
  
• Use attributes like emotion, age, and gender for personalization
  
• Design secure and ethical facial recognition applications
  

Face recognition has strong use cases in security, personalized user experiences, access control, and attendance systems. This module emphasizes both technical accuracy and responsible use.

This section has explored the implementation of intelligent question-answering systems using QnA Maker, the development of conversational bots through Microsoft Bot Framework, and the integration of vision capabilities using Azure’s prebuilt and custom computer vision tools.

From enabling applications to answer user questions to building responsive bots and training visual recognition models, these capabilities help software developers design richer, smarter, and more accessible digital products.

In the final part, we will explore advanced topics such as reading text from documents, creating knowledge mining solutions, and best practices for securing, deploying, and monitoring AI applications in production environments.

Document Intelligence, Knowledge Mining, and Operationalizing AI Solutions

As AI projects mature, the focus shifts from building individual capabilities to creating end-to-end intelligent systems that extract insights from documents, structure unstructured data, and run reliably in production environments. This final part covers advanced Azure AI capabilities, including document intelligence, knowledge mining with Azure Cognitive Search, and the operational aspects of securing, deploying, and monitoring AI solutions.

These topics ensure developers are equipped not just to build models, but to integrate them into real-world applications that are scalable, secure, and manageable.

Module 11: Reading Text in Images and Documents

This module introduces Azure’s OCR (Optical Character Recognition) services, which allow developers to extract printed and handwritten text from scanned documents, PDFs, and images.

Lessons include using Azure’s Read API to scan documents for text, including support for multi-page documents and complex layouts like tables and columns. The module also explains how to extract structured content using the Azure Form Recognizer service.

Labs involve submitting images and scanned PDFs to the Read API and parsing the returned JSON structure. Students also train a custom form model using labeled documents and extract key-value pairs for automation scenarios like invoice processing.

By the end of this module, learners will be able to extract readable and structured text from documents, build automated workflows that replace manual data entry, and support use cases like digitization, data archiving, and regulatory compliance.

Module 12: Creating Knowledge Mining Solutions

This module explores how to build enterprise-grade search and discovery systems using Azure Cognitive Search combined with AI enrichment.

Students learn to ingest and index large volumes of content such as PDFs, images, emails, and web pages. They apply AI skills like OCR, language detection, entity recognition, and key phrase extraction to enrich the content and make it searchable.

The labs walk through creating a cognitive search index, applying enrichment steps, and testing the search experience. Learners also integrate external AI models into the enrichment pipeline.

By the end of this module, students will be able to build solutions that surface hidden insights from unstructured content, power internal search engines, and support applications like legal research, customer support analysis, and knowledge base development.

Module 13: Monitoring and Securing Azure AI Services

As AI solutions move into production, monitoring, governance, and security become critical. This module covers best practices for managing AI workloads in a secure and maintainable way.

Students learn to configure diagnostics and alerts for AI services, audit usage, and monitor model performance over time. The module explains how to use Azure Monitor, Application Insights, and metrics to ensure services remain reliable and cost-effective.

Security topics include managing keys and access control with Azure Key Vault and RBAC, encrypting sensitive data, and applying network restrictions for AI resources.

By the end of this module, learners will be able to monitor deployed AI services, enforce access policies, track usage patterns, and troubleshoot issues in real time, ensuring that AI applications meet enterprise requirements for reliability and governance.

Module 14: Deploying and Managing AI Applications

This final module focuses on how to operationalize AI solutions in production environments. It includes guidance on choosing between container-based deployment and managed services, managing versioned models, and automating deployment workflows.

Students explore how to deploy models using Azure Kubernetes Service (AKS), Azure App Services, or container registries. They also learn how to implement CI/CD pipelines for AI models, update endpoints safely, and handle rollback scenarios.

By completing the labs, learners practice deploying a model to a container, updating it via Azure DevOps, and ensuring that changes can be tested and released without service disruption.

At the end of this module, learners are equipped to build production-ready systems that incorporate AI features, scale effectively, and support continuous improvement cycles.

Final Thoughts

The AI-102 course brings together a wide range of Azure AI services and practical design strategies to help developers build intelligent, reliable, and secure applications. From language understanding and Q&A bots to vision models, document intelligence, and full-scale deployment strategies, the course prepares learners to create real-world AI solutions.

Throughout the four parts, students progress from foundational knowledge to advanced implementation. They gain the ability to design conversational systems, analyze visual data, automate document processing, mine knowledge from unstructured content, and operationalize AI in a secure and governed environment.

With this training, developers are well-positioned to pass the AI-102 certification exam and take on professional roles in AI development, solution architecture, and intelligent application design.