Course Price: Original price was: $2,000.00.$1,500.00Current price is: $1,500.00.
Course Price
Original price was: $2,000.00.$1,500.00Current price is: $1,500.00.
25% OFF. Expires in
Develop AI Apps and Agents on Azure (AI-103)
AI-103: Developing AI Apps and Agents course is aligned with the ai-103 certification training and focuses on the full development lifecycle of AI-powered applications, from deploying and optimizing generative AI models to building autonomous agents that connect knowledge sources, tools, and external services. Learners explore how to implement Retrieval-Augmented Generation (RAG) pipelines, integrate APIs and SDKs, and orchestrate agentic workflows that reason across text, documents, images, and other multimodal inputs. The ai-103 certification training course also introduces AI agents and multi-agent orchestration, enabling developers to design applications that can plan, act, and collaborate across systems. Participants learn how to ground AI responses using enterprise data, apply responsible AI safeguards, and evaluate outputs to ensure reliability, security, and compliance.
This course is aligned with the Microsoft’s role-based certification, this ai-103 certification training prepares developers for the Microsoft Certified: Azure AI Apps and Agents Developer Associate credential. It is ideal for professionals who want to move from isolated AI features to production-grade AI apps and agent-based solutions built on Azure. By the end of this course, learners will be able to:
- Build production-ready AI apps using Microsoft Foundry and Azure AI services
- Develop generative AI solutions with RAG pipelines and knowledge grounding
- Design and implement AI agents and agentic workflows using APIs and SDKs
- Apply responsible AI, evaluation, and safety controls in AI solutions
- Prepare confidently for the AI-103 certification training and exam
Who should attend the AI-103: Developing AI Apps and Agents on Azure course ?
For Professionals
This course is best suited for professionals who design, build, or integrate AI-powered applications and agents on Azure, and want practical, production-ready skills.
For Businesses
Organizations should nominate professionals who are responsible for building, scaling, or governing AI-powered solutions, especially where Copilot, agents, or generative AI are part of the roadmap.
Prerequisites for the "AI-103: Developing AI Apps and Agents on Azure" Course
Before attending this course, students should have:
- Familiarity with Python programming
- Experience using REST APIs and SDKs to build cloud-connected applications
- Foundational knowledge of Azure services and the Azure portal
- Understanding of basic AI/ML concepts such as machine learning models and inferencing.
Key Features of Flexmind's AI-103: Developing AI Apps and Agents on Azure Training
This training is delivered by Flexmind through flexible online and offline formats and is designed to align with the most current certification exam requirements. The key features of this training are as follows:
Course Duration
The course has a total duration of 32 hours and is completed over 4 days.
Instructor-Led Training
Delivered by a senior Microsoft Certified Trainer with real-world, enterprise-scale experience in Microsoft AI based implementations.
Microsoft Official curriculum
Delivered by Flexmind using official Microsoft curriculum, this program blends study material, hands-on labs, and applied workshops with instructor-led guidance throughout.
Cloud Lab Access
The course will be covered using cloud lab access.
Course Completion Certificate
Course completion includes certification, formally validating the skills gained and reinforcing professional credibility.
Course Outline - Develop AI Apps and Agents on Azure (AI-103)
Module 1: Develop generative AI apps in Azure
- Understand generative AI workloads on Azure and how they differ from traditional machine learning approaches.
- Explore Microsoft Foundry as a unified, project-based platform for building AI applications using models, tools, and knowledge.
- Select, deploy, and evaluate foundation models from the Microsoft Foundry model catalog using benchmarks for quality, safety, cost, and throughput.
- Build and test generative AI chat applications using the model playground, SDKs, and the modern Responses API.
- Apply responsible AI principles including content safety, evaluation, and guardrails to ensure reliable and compliant AI app behavior.
- Lab: Build a generative AI chat app using Azure and Microsoft Foundry, including model deployment, prompt testing, and application integration.
Module 2: Developing Azure AI Apps and Agents
- Understand the concept of AI agents and how they combine large language models (LLMs), instructions, tools, and memory to perform intelligent tasks.
- Explore common AI agent use cases such as personal productivity, research, sales automation, and customer service workflows.
- Learn the end-to-end development workflow for Azure AI agents, from creating a Microsoft Foundry project to deploying agents into production applications.
- Compare development approaches using the Microsoft Foundry Portal for visual configuration and Visual Studio Code for code-first, Git-integrated development.
- Integrate custom tools with AI agents using function calling, Azure Functions, OpenAPI specifications, and low-code workflows to extend agent capabilities.
- Understand how to connect agents to enterprise knowledge using Retrieval Augmented Generation (RAG) and managed knowledge platforms such as Foundry IQ.
- Design and orchestrate multi-agent solutions using workflow patterns such as sequential, group chat, handoff, and concurrent orchestration.
- Lab: Develop an Azure AI agent using Microsoft Foundry and Visual Studio Code, including agent creation, tool integration, and execution
Module 3: Develop natural language solutions in Azure
- Understand how natural language workloads fit within the broader Azure AI ecosystem, alongside generative AI, speech, vision, and information extraction.
- Analyze text using Azure Language in Microsoft Foundry Tools, including language detection, named entity recognition, and personally identifiable information (PII) detection.
- Build text analysis solutions using prebuilt language models without custom training, accessed through unified Foundry endpoints and SDKs.
- Develop agent-based natural language solutions by exposing Azure Language capabilities as tools through the Model Context Protocol (MCP).
- Create speech-enabled AI applications that support speech-to-text, text-to-speech, multilingual translation, and real-time conversational voice interactions.
- Understand how Azure Translator and Azure Speech services enable text and speech translation for multilingual AI applications.
- Lab: Develop a text analysis agent using Azure Language MCP Server, including tool configuration, agent testing, and client application integration.
Module 4: Extract insights from visual data in Azure
- Understand how visual workloads such as computer vision and information extraction fit into the broader Azure AI architecture.
- Extract insights from images using vision-capable multimodal AI models that can analyze both text and visual inputs.
- Build vision-enabled generative AI applications to interpret images using image-based prompts and multimodal inference APIs.
- Generate images and videos from natural language prompts using image and video generation models available in Microsoft Foundry.
- Analyze images, documents, audio, and video using Azure Content Understanding to extract structured information without custom model training.
- Create custom analyzers with Azure Content Understanding using schemas to extract domain-specific data from multimodal content.
- Lab: Extract information from images and multimodal content using Azure Content Understanding, including analyzer creation and result interpretation
Class Schedule
Instructor‑Led Training
- 32-Hour of Instructor‑Led Training
- One‑to‑one doubt‑resolution sessions
- Microsoft Official Lab Access
Learning Objectives - Develop AI Apps and Agents on Azure (AI-103)
After completing the AI-103 course, learners will be able to:
- Design and build intelligent AI applications using Azure AI services, generative AI models, and Microsoft AI Foundry following Microsoft-recommended architectures.
- Develop and deploy AI agents that use tools, knowledge, and memory to perform multi-step reasoning and automation tasks in enterprise scenarios.
- Integrate generative AI models into applications using the Responses API, multimodal prompts, and Azure SDKs for text, vision, and speech workloads.
- Implement enterprise-ready AI solutions with responsible AI principles, including security, governance, evaluation, and cost-aware deployment strategies.
- Extend applications with agentic and automation capabilities by integrating Azure AI services, custom tools, MCP-based tool orchestration, and cloud-native workflows.
About AI-103: Developing AI Apps and Agents on Azure Certification Exam
To help you understand the assessment better, here are a few important details about the exam.
| Exam Name | AI-103: Developing AI Apps and Agents on Azure |
|---|---|
| Who should Apply | AI Engineer, Developer |
| Duration of Exam | 120 Minutes |
| Fees | Rs. 4,865 (India), $165 USD (United States) |
| Level of Difficulty | Intermediate |
| Type of Credential | Microsoft Certification |
| Languages | English, Japanese, Chinese (Simplified), German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia) |
| Exam Retake | Exam retake allowed after 24 hours |
| Quality Check during Assessment | The online exam is proctored |
The table below represents the weightage of each study area in the exam. Areas with higher percentages are expected to have more questions.
| Study Area | Percentage |
|---|---|
| Plan and manage an Azure AI solution | 25-30% |
| Implement generative AI and agentic solutions | 30-35% |
| Implement computer vision solutions | 10-15% |
| Implement text analysis solutions | 10-15% |
| Implement information extraction solutions | 10-15% |
