{"id":253302,"date":"2026-04-09T00:14:48","date_gmt":"2026-04-08T22:14:48","guid":{"rendered":"https:\/\/www.itta.net\/?post_type=formations&#038;p=253302"},"modified":"2026-05-20T13:22:25","modified_gmt":"2026-05-20T11:22:25","slug":"develop-ai-cloud-solutions-on-azure-ai-200","status":"publish","type":"formations","link":"https:\/\/www.itta.net\/en\/formations\/develop-ai-cloud-solutions-on-azure-ai-200\/","title":{"rendered":"Develop AI cloud solutions on Azure (AI-200)"},"content":{"rendered":"<p>D\u00e9velopper des solutions IA cloud robustes dans Azure exige de ma\u00eetriser \u00e0 la fois les services Azure AI, l&#8217;architecture cloud-native et les bonnes pratiques de mise en production. La formation AI-200 couvre l&#8217;ensemble du cycle de vie : conception, d\u00e9veloppement, d\u00e9ploiement et exploitation de solutions IA g\u00e9n\u00e9ratives et conventionnelles dans Azure.<\/p>\n<p>Pendant cinq jours, vous travaillez sur Azure AI Foundry, Azure OpenAI Service, Azure AI Search, les agents intelligents, le RAG, et l&#8217;int\u00e9gration avec les services data Azure (Cosmos DB, SQL, Storage). La formation est anim\u00e9e \u00e0 Gen\u00e8ve et Lausanne par un formateur Microsoft Certified Trainer.<\/p>\n","protected":false},"featured_media":0,"parent":0,"menu_order":0,"template":"","editeurs":[4277,1335],"domaine":[4244,2980,4272],"class_list":["post-253302","formations","type-formations","status-publish","hentry","editeurs-azure-ai-services","editeurs-microsoft","domaine-developpement-ia-et-llm","domaine-intelligence-artificielle","domaine-plateformes-ia-azure"],"acf":{"reference":"AI-200T00","duree_nombre":5,"duree_unite":"jour","prix_virtuel":3500,"prix_presentiel":3700,"prix_blended":"","prix_elearning":"","views":35,"niveau":["Interm\u00e9diaire"],"role":[],"certifiant":["Certifiant"],"pdus":"","lieux":["Gen\u00e8ve","Lausanne"],"formats_dapprentissage":["presentiel","virtuel"],"examen_inclus":"Optionnel","garanti":[],"financement":["Temptraining"],"description_temptraining":"","description_caf":"","description_title":"Develop AI cloud solutions on Azure Training (AI-200)","objectifs":"<ul>\n<li>Architect an end-to-end AI solution in Azure following Well-Architected Framework principles<\/li>\n<li>Develop generative applications with Azure OpenAI Service and the Azure AI SDK<\/li>\n<li>Build AI agents orchestrating tools, data sources and workflows<\/li>\n<li>Implement complete RAG pipelines with Azure AI Search and vector embeddings<\/li>\n<li>Industrialize AI deployment with Azure MLOps (model registry, monitoring, drift detection)<\/li>\n<li>Secure cloud AI solutions with content safety, managed identity and private networking<\/li>\n<\/ul>","connaissances":"","profils_participants":"","examen":"<p>This course prepares you to the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam.<\/p>","documentation":"","lab__exercices":"","contenu_cours":"<p><strong>Module 1 : Store and manage containers in Azure Container Registry<\/strong><\/p>\n<ul>\n<li>Registries, repositories, and artifacts<\/li>\n<li>Build and run images with ACR Tasks<\/li>\n<li>Tag and version images<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 2 : Deploy containers to Azure App Service<\/strong><\/p>\n<ul>\n<li>Deploy containers to Azure App Service<\/li>\n<li>Configure container runtime behavior<\/li>\n<li>Configure application settings<\/li>\n<li>Observe and troubleshoot containerized apps<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 3 : Deploy containers to Azure Container Apps<\/strong><\/p>\n<ul>\n<li>Explore Container Apps environments<\/li>\n<li>Deploy a container app using the Azure CLI and YAML<\/li>\n<li>Configure runtime settings with environment variables and secrets<\/li>\n<li>Configure image pull authentication for private registries<\/li>\n<li>Verify deployments with logs and status<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 4 : Manage containers in Azure Container Apps<\/strong><\/p>\n<ul>\n<li>Update images and manage revisions safely<\/li>\n<li>Manage the container app lifecycle<\/li>\n<li>Monitor logs and troubleshoot issues<\/li>\n<li>Configure health probes and troubleshoot failures<\/li>\n<li>Optimize container resources and scaling<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 5 : Scale containers in Azure Container Apps<\/strong><\/p>\n<ul>\n<li>Configure scale rules<\/li>\n<li>Implement event-driven scaling with KEDA<\/li>\n<li>Apply KEDA scalers for custom workloads<\/li>\n<li>Select compute resources for performance and cost<\/li>\n<li>Choose and apply revision modes<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 6 : Deploy applications to Azure Kubernetes Service<\/strong><\/p>\n<ul>\n<li>Create Kubernetes deployment manifests<\/li>\n<li>Expose applications in Azure Kubernetes Services<\/li>\n<li>Deploy applications to Azure Kubernetes Services<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 7 : Configure applications on Azure Kubernetes Service<\/strong><\/p>\n<ul>\n<li>Define ConfigMaps for application settings<\/li>\n<li>Implement secrets for sensitive data<\/li>\n<li>Attach persistent storage to an app<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 8 : Monitor and troubleshoot applications on Azure Kubernetes Service<\/strong><\/p>\n<ul>\n<li>Monitor application logs and metrics<\/li>\n<li>Troubleshoot pods and services<\/li>\n<li>Verify service connectivity and endpoints<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 9 : Build queries for Azure Cosmos DB for NoSQL<\/strong><\/p>\n<ul>\n<li>Explore Azure Cosmos DB for NoSQL<\/li>\n<li>Implement the Azure Cosmos DB for NoSQL SDK<\/li>\n<li>Query Azure Cosmos DB for NoSQL<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 10 : Implement vector search on Azure Cosmos DB for NoSQL<\/strong><\/p>\n<ul>\n<li>Store and retrieve embeddings in Azure Cosmos DB<\/li>\n<li>Execute vector similarity queries for semantic search<\/li>\n<li>Combine vector similarity results with metadata filtering<\/li>\n<li>Use the change feed to trigger embedding refresh<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 11 : Optimize query performance for Azure Cosmos DB for NoSQL<\/strong><\/p>\n<ul>\n<li>Understand indexes in Azure Cosmos DB<\/li>\n<li>Configure range and composite indexes<\/li>\n<li>Tune vector indexes for embedding workloads<\/li>\n<li>Reduce RU costs with strategic indexing<\/li>\n<li>Choose consistency levels for optimal performance<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 12 : Build and query with Azure Database for PostgreSQL<\/strong><\/p>\n<ul>\n<li>Explore Azure Database for PostgreSQL<\/li>\n<li>Connect to PostgreSQL<\/li>\n<li>Create and manage schemas<\/li>\n<li>Query data<\/li>\n<li>Integrate SDKs and applications<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 13 : Implement vector search with Azure Database for PostgreSQL<\/strong><\/p>\n<ul>\n<li>Store and query embeddings with pgvector<\/li>\n<li>Perform fast vector similarity search<\/li>\n<li>Manage index lifecycle and embedding updates<\/li>\n<li>Run vector similarity search for semantic retrieval<\/li>\n<li>Implement retrieval patterns for RAG pipelines<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 14 : Optimize vector search in Azure Database for PostgreSQL<\/strong><\/p>\n<ul>\n<li>Tune PostgreSQL for pgvector<\/li>\n<li>Choose and configure vector indexes<\/li>\n<li>Optimize data layout<\/li>\n<li>Scale for high-volume workloads<\/li>\n<li>Connection optimization<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 15 : Implement data operations in Azure Managed Redis<\/strong><\/p>\n<ul>\n<li>Explore Azure Managed Redis<\/li>\n<li>Client libraries and development best practices<\/li>\n<li>Implement data operations<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 16 : Implement event messaging with Azure Managed Redis<\/strong><\/p>\n<ul>\n<li>Publish and subscribe to events with Redis pub\/sub<\/li>\n<li>Implement task queues with Redis Streams<\/li>\n<li>Choose between broadcast and coordinated distribution<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 17 : Implement vector storage in Azure Managed Redis<\/strong><\/p>\n<ul>\n<li>Index and query vector data<\/li>\n<li>Choose vector types and indexing strategies<\/li>\n<li>Optimize Redis data structures for vector storage<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 18 : Queue and process AI operations with Azure Service Bus<\/strong><\/p>\n<ul>\n<li>Explore Azure Service Bus concepts and messaging in AI architectures<\/li>\n<li>Choose between queues and topics with subscriptions<\/li>\n<li>Structure messages for AI workloads<\/li>\n<li>Process messages reliably<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 19 : Develop event-driven AI workflows with Azure Event Grid<\/strong><\/p>\n<ul>\n<li>Understand Azure Event Grid concepts and event-driven patterns for AI solutions<\/li>\n<li>Work with event schemas and properties<\/li>\n<li>Configure delivery and retry policies for reliable event processing<\/li>\n<li>Publish custom events from AI applications<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 20 : Build serverless AI backends with Azure Functions<\/strong><\/p>\n<ul>\n<li>Understand Azure Functions hosting and scaling for AI workloads<\/li>\n<li>Set up the local development environment for Functions<\/li>\n<li>Create triggers and bindings for AI integration patterns<\/li>\n<li>Manage secrets and configuration in Functions<\/li>\n<li>Configure identity and access for Functions<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 21 : Manage application secrets with Azure Key Vault<\/strong><\/p>\n<ul>\n<li>Store and organize secrets, keys, and certificates<\/li>\n<li>Retrieve secrets using Azure SDK client libraries<\/li>\n<li>Handle secret versioning and rotation<\/li>\n<li>Implement caching strategies to reduce Key Vault calls<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 22 : Manage application settings with Azure App Configuration<\/strong><\/p>\n<ul>\n<li>Connect to App Configuration from application code<\/li>\n<li>Organize settings with labels and feature flags<\/li>\n<li>Reference Key Vault secrets from App Configuration<\/li>\n<li>Decide what to store in App Configuration vs Key Vault<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 23 : Instrument an app with OpenTelemetry<\/strong><\/p>\n<ul>\n<li>Explore OpenTelemetry and its role in observability<\/li>\n<li>Add the OpenTelemetry SDK to an application<\/li>\n<li>Configure spans and traces<\/li>\n<li>Export telemetry to Azure Monitor<\/li>\n<li>Debug distributed flows with trace data<\/li>\n<li>Module assessment<\/li>\n<\/ul>\n<p><strong>Module 24 : Analyze app telemetry with logs and metrics<\/strong><\/p>\n<ul>\n<li>Write basic KQL queries<\/li>\n<li>Explore logs for errors and performance<\/li>\n<li>Build dashboards for app telemetry<\/li>\n<li>Create workbooks for interactive analysis<\/li>\n<li>Set alerts for app failures and anomalies<\/li>\n<li>Module assessment<\/li>\n<\/ul>","cours_recommandes":null,"infos_additionnelles":"<h3>Why the Develop AI cloud solutions on Azure (AI-200) training<\/h3>\n<p>Companies are industrializing their AI initiatives and demand architects able to design robust cloud-native AI solutions. AI-200 goes beyond Jupyter experimentation: you address production, observability, security and compliance. The course is for those who transform AI proof-of-concepts into scalable services in production on Azure.<\/p>\n\n<h3>Azure AI Foundry and foundation models<\/h3>\n<p>Azure AI Foundry is the development hub. The training has you deploy models from the model catalog (GPT-4o, Phi, Mistral, Llama), evaluate their performance on your datasets, and orchestrate complex workflows with prompt flow. You also work on supervised fine-tuning to adapt models to your business use cases.<\/p>\n\n<h3>AI agents and orchestrated architectures<\/h3>\n<p>The program covers agent design with Azure AI Agent Service and multi-agent patterns. You build agents able to reason on plans, invoke function calls to external APIs, and cooperate to solve complex tasks. Design principles (ReAct, Plan-and-Execute, tool-augmented LLM) are detailed.<\/p>\n\n<h3>Enterprise-grade RAG with Azure AI Search<\/h3>\n<p>RAG is treated in depth: hybrid vector + lexical search, semantic ranking, query rewriting, contextual chunking, and systematic evaluation with groundedness and relevance metrics. You compare strategies (basic RAG vs agentic RAG vs graph RAG) and choose according to the use case.<\/p>\n\n<h3>MLOps and industrialization<\/h3>\n<p>AI industrialization is not optional: the training addresses Azure Machine Learning model registry, production monitoring (latency, cost, drift), automated retraining and CI\/CD integration with Azure DevOps and GitHub Actions. The goal is to reach a reproducible and auditable AI pipeline.<\/p>\n\n<h3>Security, identity and private networking<\/h3>\n<p>Securing a cloud AI solution goes through Managed Identities, private endpoints (to avoid public traffic), Azure Key Vault for secrets, and Content Safety to filter LLM outputs. The training also covers conditional access controls and Microsoft Entra ID integration.<\/p>\n\n<h3>Audience and prerequisites<\/h3>\n<p>The Develop AI cloud solutions on Azure (AI-200) training targets solution architects, experienced AI developers and ML engineers who will design production AI systems. Prerequisites: Python experience, Azure fundamentals (AZ-900), AI knowledge (AI-900). Prior experience with Azure OpenAI or Azure AI Search is a plus.<\/p>\n\n<h3>FAQ Develop AI cloud solutions on Azure (AI-200)<\/h3>\n<p><strong>What's the difference between AI-200 and AI-102 \/ AI-103?<\/strong><\/p>\n<p>AI-200 is more architecture and end-to-end solution oriented (5 days), while AI-103 \/ AI-102 are oriented toward application development (4 days). AI-200 includes MLOps, advanced security and enterprise multi-agent architectures.<\/p>\n\n<p><strong>Does the training cover Azure Machine Learning?<\/strong><\/p>\n<p>Yes, with MLOps focus: model registry, managed online endpoints deployment, monitoring and CI\/CD integration. Classic data science (model training) is addressed transversally.<\/p>\n\n<p><strong>Do I need prior AI experience to take AI-200?<\/strong><\/p>\n<p>Prior experience of at least 6 months in AI development or Azure data engineering is strongly recommended. AI-103 or AI-102 is an entry path.<\/p>\n\n<p><strong>Does the AI-200 course lead to a Microsoft certification?<\/strong><\/p>\n<p>AI-200 prepares for the Microsoft Certified: Azure AI Engineer Associate certification (AI-102 exam). The training goes beyond the exam program and covers architecture topics not formally tested.<\/p>","inclus_dans_ce_cours_virtuel":"","inclus_dans_ce_cours_presentiel":"","inclus_dans_ce_cours_blended":"","inclus_dans_ce_cours_elearning":"","inclus_dans_ce_cours_surmesure":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Develop AI cloud solutions on Azure Training (AI-200) - ITTA<\/title>\n<meta name=\"description\" content=\"AI-200 Training: architect and develop cloud-native AI solutions in Azure, RAG, agents, MLOps. 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