AI-200 Training Course – Develop AI Cloud Solutions on Microsoft Azure
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Description of the training: AI-200 Training Course – Develop AI Cloud Solutions on Microsoft Azure
The Microsoft AI-200 (Azure AI Engineer Associate) certification validates your skills to design and develop AI solutions on Azure: Azure AI Services (Vision, Language, Speech, OpenAI), Azure AI Search, Document Intelligence, and agent orchestration with Azure AI Foundry. MFE-IT prepares you in 5 days (30h) with hands-on cases in Azure labs.
What is the difference between AI-102 and AI-200?AI-102 (the previous version) covered classic Cognitive Services and basic Azure OpenAI. The newer AI-200 focuses on Azure AI Foundry, multi-modal agent orchestration, RAG with AI Search, and production GenAI scenarios (guardrails, monitoring, evaluation). It reflects Azure’s evolution towards an AI agent platform.
What are the prerequisites for the AI-200 training?A good command of Python or C#, solid Azure fundamentals (equivalent to AZ-900), and familiarity with REST APIs and the cloud. Prior knowledge of AI/ML concepts is a plus but not essential. The MFE-IT training starts with a refresher on Azure AI services then progresses to advanced scenarios. Max. 3 participants per session.
Does the course cover Azure Kubernetes Service (AKS), Cosmos DB and event-driven AI architectures?Yes. The programme covers hosting containerised applications on Azure, Azure Kubernetes Service (AKS) for AI applications, databases for AI (Cosmos DB, PostgreSQL), event-driven architectures and AI orchestration, as well as security, monitoring and best practices.
Do I need to know how to deploy containers and use Azure beforehand?Basic Azure and container knowledge is recommended: the course goes deeper into deploying containerised AI applications on AKS. The AI-102 (Azure AI Engineer) level is a good foundation.
How long does it take to prepare for the AI-200 exam?The MFE-IT training lasts 5 days (30 hours), in a fully tailored format. It covers all the exam objectives: planning and managing an Azure AI solution, implementing vision, NLP and speech solutions, document extraction, generative AI with Azure OpenAI, and agent orchestration. 30 days of post-training support to pass your certification.
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Our sessions are guaranteed from a single registrant, with no risk of postponement (except in cases of force majeure). A preliminary interview takes place between the participant and/or a company representative in order to fully account for the participant’s profile (level, needs, professional context, challenges, etc.). Assessment: during the training, the trainer assesses participants’ learning progress through quizzes, practical scenarios and hands-on labs. Participants receive a certificate of attainment at the end of the training.
This training is part of our Artificial Intelligence Training catalogue. Discover our other AI courses to fully harness the potential of machine learning, LLMs and generative AI.
Objectives of the training: AI-200 Training Course – Develop AI Cloud Solutions on Microsoft Azure
On completion of the training, participants will be able to:
- Deploy and manage containerised applications on Azure using Azure Container Registry, Azure Container Apps and Azure Kubernetes Service.
- Develop AI solutions leveraging Azure Cosmos DB for NoSQL and Azure Database for PostgreSQL, including vector search and performance optimisation.
- Design event-driven architectures with Azure Event Grid, Azure Functions and Azure Service Bus to orchestrate AI workflows.
- Integrate Azure AI Services (OpenAI, Cognitive Services) into secure, scalable back-end applications.
- Secure AI applications with Azure Key Vault, managed identities and network security policies.
- Monitor and troubleshoot AI applications in production using Azure Monitor, Application Insights and diagnostic tools.
Prerequisites of the training: AI-200 Training Course – Develop AI Cloud Solutions on Microsoft Azure
To get the most out of this training, programming experience with Python, JavaScript or C# is required. An understanding of Azure services and the fundamental concepts of cloud computing is also required.
Knowledge of containerisation (Docker), use of the Azure CLI, JSON structures and Machine Learning concepts (embeddings, similarity search) is strongly recommended.
Because every participant is unique, a personalised interview with our expert allows us to design a training course perfectly aligned with their goals, level and professional challenges.
Detailed programme of the training: AI-200 Training Course – Develop AI Cloud Solutions on Microsoft Azure
Hosting containerised applications
- Store and manage container images in Azure Container Registry.
- Deploy containers on Azure App Service for simplified hosting.
- Deploy and manage applications on Azure Container Apps: automatic scaling and revision management.
- Configure environment variables, secrets and scaling rules in Azure Container Apps.
Azure Kubernetes Service (AKS) for applications
- Deploy applications on Azure Kubernetes Service: creating clusters, pods and services.
- Configure applications on AKS: ConfigMaps, Secrets, persistent volumes and Ingress Controllers.
- Monitor and troubleshoot applications on AKS with Azure Monitor and Kubernetes metrics.
- Implement security and automatic scaling best practices in AKS.
Databases for AI: Cosmos DB and PostgreSQL
- Develop AI solutions with Azure Cosmos DB for NoSQL: queries, partitioning and optimisation.
- Implement vector search on Azure Cosmos DB for semantic AI applications.
- Generate and query data with Azure Database for PostgreSQL.
- Implement and optimise vector search in PostgreSQL with the pgvector extension.
- Choose the right database service for different AI scenarios.
Event-driven architectures and orchestration
- Design event-driven architectures with Azure Event Grid to trigger AI workflows.
- Develop Azure Functions for serverless processing and AI service integration.
- Orchestrate data pipelines and AI workflows with Azure Logic Apps and Durable Functions.
- Integrate Azure AI Services (OpenAI, Cognitive Services) into application workflows.
- Manage asynchronous messaging with Azure Service Bus and Azure Queue Storage.
Security, monitoring and best practices
- Secure AI applications with Azure Key Vault for centralised management of secrets and certificates.
- Implement managed identities and role-based access control (RBAC) for Azure services.
- Configure network security policies: VNets, NSGs, Private Endpoints and Azure Front Door.
- Monitor applications in production with Azure Monitor, Application Insights and Azure Log Analytics.
- Set up alerts, monitoring dashboards and troubleshooting strategies for AI applications.
Course highlights
- Focuses on designing AI cloud solutions
- Covers cognitive services, security and best practices
- Builds on concrete use cases
- Prepares you for the Microsoft AI-200 exam
FAQ
What is the Microsoft AI-200 certification?
The Microsoft AI-200 (Azure AI Engineer Associate) certification validates your skills to design and develop AI solutions on Azure: Azure AI Services (Vision, Language, Speech, OpenAI), Azure AI Search, Document Intelligence, and agent orchestration with Azure AI Foundry. MFE-IT prepares you in 5 days (30h) with hands-on cases in Azure labs.
What is the difference between AI-102 and AI-200?
AI-102 (the previous version) covered classic Cognitive Services and basic Azure OpenAI. The newer AI-200 focuses on Azure AI Foundry, multi-modal agent orchestration, RAG with AI Search, and production GenAI scenarios (guardrails, monitoring, evaluation). It reflects Azure’s evolution towards an AI agent platform.
What are the prerequisites for the AI-200 training?
A good command of Python or C#, solid Azure fundamentals (equivalent to AZ-900), and familiarity with REST APIs and the cloud. Prior knowledge of AI/ML concepts is a plus but not essential. The MFE-IT training starts with a refresher on Azure AI services then progresses to advanced scenarios. Max. 3 participants per session.
Does the course cover Azure Kubernetes Service (AKS), Cosmos DB and event-driven AI architectures?
Yes. The programme covers hosting containerised applications on Azure, Azure Kubernetes Service (AKS) for AI applications, databases for AI (Cosmos DB, PostgreSQL), event-driven architectures and AI orchestration, as well as security, monitoring and best practices.
Do I need to know how to deploy containers and use Azure beforehand?
Basic Azure and container knowledge is recommended: the course goes deeper into deploying containerised AI applications on AKS. The AI-102 (Azure AI Engineer) level is a good foundation.
How long does it take to prepare for the AI-200 exam?
The MFE-IT training lasts 5 days (30 hours), in a fully tailored format. It covers all the exam objectives: planning and managing an Azure AI solution, implementing vision, NLP and speech solutions, document extraction, generative AI with Azure OpenAI, and agent orchestration. 30 days of post-training support to pass your certification.