AI-200 Develop AI cloud solutions on Azure Course
Course Overview
The AI‑200: Develop AI cloud solutions on Azure course provides the knowledge and skills necessary to design, develop, and implement artificial intelligence solutions in the cloud using Microsoft Azure.
Throughout the training, students will learn to work with key Azure AI services, including Azure AI Services, Azure OpenAI, natural language processing, computer vision, and intelligent agents, creating scalable and integrated solutions in enterprise environments.
This course is aimed at developing intelligent applications that automate processes, analyze information, and improve decision-making, being key for professionals working on digital transformation projects based on AI.
Virtual course with certification exam included as a gift. Don't miss this opportunity! The exam is valued at €126 + VAT and is included at no additional cost.
Promotion valid until June 30, 2026. One-attempt exam available only in Virtual - Tele-training mode. Not applicable to Self-Learning mode.
What the official Microsoft course at Nanfor includes
The course includes official Microsoft Learn material, expert tutor presentations, official labs, specialized tutoring, personalized sessions with an expert tutor, certification preparation, and a completion certificate, combining official content with expert support from Nanfor.
Learn about all components
Advantages of AI-200 training
Official Microsoft training in artificial intelligence: Updated content based on the most advanced Azure AI services.
Specialization in developing AI solutions in the cloud: You will learn to design scalable intelligent applications on Azure.
Mastery of key technologies such as Azure OpenAI and Azure AI Services: Work with language models, vision, text analysis, and automation.
Practical approach oriented to real projects: Development of solutions applicable in business environments.
High professional demand in AI and Cloud: Training aligned with one of the fastest-growing areas of the IT market.
Prerequisites
To get the most out of this course, it is recommended:
- Basic programming knowledge
- Familiarity with Azure concepts and cloud services
- General notions of artificial intelligence or data analysis (recommended, not mandatory)
Preparation for the Azure AI Cloud Developer Associate certification exam
This AI‑200 course prepares for official Microsoft certification in developing artificial intelligence solutions in Azure (Microsoft Certified: Azure AI Cloud Developer Associate), providing the necessary knowledge to design, build, and optimize AI-based applications in the cloud.
Level: Intermediate
Role: Developer
Product: Azure
Subject: Business applications
⏱️
Course Duration:
100 hours
🔑
Classroom Access:
3 months
General Course Information
🎓 Who is this course for?
This course is aimed at:
- Developers who want to specialize in artificial intelligence on Azure
- Software engineers who work with cloud solutions
- IT professionals interested in incorporating AI into their applications
- Technical profiles who wish to evolve into AI Developer roles
🎯 Training Objectives What will you learn?
Upon completion of the AI‑200 course, participants will be able to:
- Develop artificial intelligence solutions in Microsoft Azure
- Use Azure AI Services and Azure OpenAI in real projects
- Implement natural language processing (NLP) solutions
- Create computer vision applications and image analysis
- Design scalable AI architectures in the cloud
- Integrate artificial intelligence into enterprise applications
📚 Elements of the AI-200 collection from Microsoft Learn
- Introduction to Azure AI and cognitive services
- Developing solutions with Azure OpenAI Service
- Implementing natural language processing (NLP)
- Text analysis and language understanding
- Developing computer vision solutions
- Using AI services to automate processes
- Integrating AI into cloud applications
📚 Course Content Developing Artificial Intelligence Cloud Solutions on Azure - Program
Unit 1: Implement containerized application hosting on Azure
Module 1: Store and manage containers in Azure Container Registry
Learning objectives:
- Explain how Azure Container Registry organizes images
- Create and manage container images with ACR Tasks
- Implement tagging and versioning strategies
- Use Azure CLI to manage images and tasks
- Understand production considerations in container environments
Lab / Practice:
- Building and running a container image with ACR Tasks
Module 2: Deploy containers to Azure App Service
Learning objectives:
- Deploy custom containers to Azure App Service
- Configure container runtime environment (ports, startup, storage)
- Configure application variables and connection strings
- Monitor and troubleshoot containerized applications
Lab / Practice:
- Deploying a container to Azure App Service
Unit 2: Deploy and manage applications in Azure Container Apps
Module 1: Deploy containers to Azure Container Apps
Learning objectives:
- Understand Azure Container Apps environments
- Deploy using CLI and YAML files
- Configure environment variables and secrets
- Configure authentication with the container registry
- Verify deployments using logs and revisions
Lab / Practice:
- Deploying a containerized backend API
Module 2: Manage containers in Azure Container Apps
Learning objectives:
- Manage revisions and update images
- Diagnose errors in revisions
- Monitor logs and troubleshoot issues
- Configure health probes
- Optimize resources and scaling
Lab / Practice:
- Diagnosing and resolving a failed deployment
Module 3: Scale containers in Azure Container Apps
Learning objectives:
- Configure scaling rules (HTTP, CPU, memory)
- Implement event-driven scaling with KEDA
- Select appropriate compute resources
- Apply revision modes to control scaling and traffic
Lab / Practice:
- Configuring autoscaling with KEDA
Unit 3: Deploy and monitor applications in Azure Kubernetes Service
Module 1: Deploy applications in Azure Kubernetes Service
Learning objectives:
- Understand Deployments, Services, and Pods
- Create Kubernetes manifests
- Deploy and verify applications with kubectl
- Troubleshoot deployment errors
Lab / Practice:
- Deploying an inference API in AKS
Module 2: Configure applications in Azure Kubernetes Service
Learning objectives:
- Use ConfigMaps for configuration
- Use Secrets for sensitive data
- Configure persistent storage (PVC)
- Apply configuration patterns in AKS
Lab / Practice:
- Configuring applications in AKS
Module 3: Monitor and troubleshoot in AKS
Learning objectives:
- Monitor logs and metrics
- Diagnose problems in pods and services
- Verify connectivity and endpoints
- Apply structured troubleshooting methodologies
Lab / Practice:
- Diagnosing applications in AKS
Unit 4: Develop AI solutions with Azure Cosmos DB for NoSQL
Module 1: Create queries in Azure Cosmos DB for NoSQL
Learning objectives:
- Understand the Cosmos DB data model
- Perform CRUD operations with the SDK
- Choose between direct reads and queries
- Build SQL queries for NoSQL
Lab / Practice:
- Building a document store for RAG
Module 2: Implement vector search in Azure Cosmos DB
Learning objectives:
- Store and retrieve embeddings
- Configure vector policies
- Execute similarity queries
- Implement hybrid search
- Use change feed to keep embeddings updated
Lab / Practice:
- Developing a semantic search application
Module 3: Optimize query performance
Learning objectives:
- Analyze query patterns and RU consumption
- Configure indexes (range, composite, vector)
- Optimize indexing policies
- Select appropriate consistency levels
Unit 5: Develop AI solutions with Azure Database for PostgreSQL
Module 1: Build and query with PostgreSQL
Learning objectives:
- Understand service architecture and features
- Configure secure connections with Entra ID and TLS
- Design database schemas
- Write efficient SQL queries
- Integrate PostgreSQL with Python applications
Lab / Practice:
- Developing a backend for AI assistants
Module 2: Implement vector search in PostgreSQL
Learning objectives:
- Store embeddings with pgvector
- Execute similarity searches
- Create vector indexes (IVFFlat, HNSW)
- Design retrieval patterns for RAG
Lab / Practice:
- Implementing vector search
Module 3: Optimize vector search
Learning objectives:
- Adjust pgvector performance
- Optimize indexing strategies
- Scale PostgreSQL for AI workloads
Unit 6: Enhance AI solutions with Azure Managed Redis
Module 1: Implement data operations in Redis
Learning objectives:
- Understand caching strategies
- Select appropriate client libraries
- Implement storage and retrieval operations
- Manage data expiration and invalidation
Lab / Practice:
- Implementing data operations in Redis
Module 2: Implement messaging with Redis
Learning objectives:
- Use pub/sub for real-time messaging
- Implement Streams as task queues
- Choose the appropriate messaging pattern
- Design processing pipelines for AI
Lab / Practice:
- Publishing and subscribing to events
Module 3: Implement vector storage in Redis
Learning objectives:
- Create vector indexes with RediSearch
- Store and query embeddings
- Develop semantic search applications
Unit 7: Integrate backend services for AI solutions
Module 1: Process AI operations with Azure Service Bus
Learning objectives:
- Understand messaging patterns
- Choosing between queues and topics
- Designing messages for AI
- Reliably processing messages with DLQ
Lab / Practice:
- Message processing with Service Bus
Module 2: Developing workflows with Event Grid
Learning objectives:
- Designing event-driven architectures
- Using the CloudEvents schema
- Configuring event filtering and routing
- Managing retries and delivery
Lab / Practice:
- Publishing and receiving events
Module 3: Building serverless backends with Azure Functions
Learning objectives:
- Evaluating hosting options
- Creating triggers and bindings
- Integrating Key Vault and App Configuration
- Applying security with managed identity
Unit 8: Managing secrets and configuration in AI solutions
Module 1: Managing secrets with Azure Key Vault
Learning objectives:
- Storing secrets, keys, and certificates
- Retrieving secrets using the SDK
- Implementing secure rotation
- Applying caching strategies
Lab / Practice:
- Managing secrets with Azure Key Vault
Module 2: Managing configuration with Azure App Configuration
Learning objectives:
- Connecting applications to App Configuration
- Managing configuration using tags
- Implementing feature flags
- Integrating with Azure Key Vault
Lab / Practice:
- Retrieving configuration and secrets
Unit 9: Monitoring and troubleshooting Azure applications
Module 1: Instrumenting applications with OpenTelemetry
Learning objectives:
- Understanding observability concepts
- Instrumenting applications with OpenTelemetry
- Creating custom traces and spans
- Exporting telemetry to Application Insights
Lab / Practice:
- Application instrumentation
Module 2: Analyzing telemetry with logs and metrics
Learning objectives:
- Writing KQL queries
- Analyzing logs and metrics
- Creating dashboards and workbooks
- Configuring alerts
Lab / Practice:
🧪 Our differentiating factor: Practical labs
| Nanfor Lab |
Technical skills developed |
Practical learning outcome |
| Setting up the Azure environment for AI |
Creating resources in Azure, managing subscriptions and AI services |
The student deploys a complete environment for developing AI solutions in the cloud |
| Developing applications with Azure OpenAI |
Integrating GPT models, embeddings, and response generation |
The student creates generative AI applications ready for real-world use |
| Implementing RAG (Retrieval-Augmented Generation) solutions |
Indexing, embeddings, vector search, and data grounding |
The student builds systems that combine AI with corporate data |
| Creating serverless APIs with Azure Functions |
Designing APIs, triggers, bindings, and serverless logic |
The student exposes AI functionalities through scalable web services |
| Developing event-driven architecture |
Using Event Grid, Service Bus, and messaging |
The student implements decoupled and scalable AI workflows |
| Deploying applications with containers |
Using Azure Container Apps and Azure Kubernetes Service (AKS) |
The student deploys AI solutions in modern cloud environments |
| Managing container images |
Using Azure Container Registry (ACR) |
The student manages versions and deployments of AI applications |
| Developing solutions with Cosmos DB (NoSQL) |
Database design, queries, and optimization |
The student manages data storage for AI applications |
| Using PostgreSQL with pgvector |
Implementing vector databases |
The student builds advanced semantic search engines |
| Implementing caching with Azure Redis |
Performance optimization and latency reduction |
The student improves the performance of AI applications in production |
| Integrating Azure AI services |
Using language, vision, speech, and content APIs |
The student integrates multiple AI capabilities into one solution |
| Security and secret management |
Using Azure Key Vault, identities, and access |
The student protects sensitive applications and data in AI environments |
| Observability and monitoring |
Using logs, metrics, OpenTelemetry, and KQL |
The student monitors performance and detects errors in AI solutions |
| Automating deployments |
Configuring basic DevOps pipelines |
The student automates the delivery of AI solutions |
| Designing scalable AI architectures |
Service selection, cloud-native design |
The student designs robust, production-oriented solutions |
| Developing end-to-end AI solution |
Integrating all previous components |
The student builds a complete application ready for an enterprise environment |
🌐 Language
- Course: English / Spanish
- Labs: English / Spanish
ℹ️ Do you want to take this course? Request information now
If you want to take this training virtually, you can purchase it at the top of the product. If you have any questions, please contact us.
If you want to take it in face-to-face or telepresence mode, please contact us:
🏢 Nanfor, Microsoft's official ICT training center
Nanfor is a customized ICT training center, specializing in technological training for professionals and companies, and is officially accredited by Microsoft as:
- Microsoft Solutions Partner – Training Services
- Microsoft Cloud Partner
These accreditations certify that Nanfor complies with Microsoft's standards for delivering technical courses, using Microsoft content and Microsoft Certified Trainers (MCTs), guaranteeing quality, continuous updates, and alignment with certifications.
❓ Frequently Asked Questions
What is the AI‑200 course?
It is an official Microsoft training that teaches how to develop artificial intelligence solutions in the cloud with Azure, using services such as Azure AI and Azure OpenAI.
What will I learn in this course?
You will learn to create intelligent applications using Azure AI services, including natural language processing, computer vision, and generative AI models.
Does the course include the certification exam?
Yes. This course includes the official Microsoft certification exam (according to the conditions of the current promotion).
Is it included in Nanfor's LaaS?
Yes. The AI‑200 course is part of LaaS Cert, which allows access to this training along with other official certifications.
How is the course conducted?
It is conducted online, with access to content, labs, and expert support, allowing for flexible progress.
Can it be subsidized by FUNDAE?
Yes. This course can be subsidized through FUNDAE, subject to company conditions.
How long is access to the course?
The course includes 3 months of access, with the possibility of extension (except for subsidized training).
