AI-200 Course: Develop AI Solutions on Microsoft Azure
The AI-200 Course: Develop AI Cloud Solutions on Azure is an official Microsoft training program aimed at professionals who want to design, develop, and deploy artificial intelligence solutions using Azure services. This training provides the knowledge needed to build intelligent applications based on generative AI models, agents, Azure AI Services, and advanced data processing services.
Microsoft Azure is Microsoft's cloud platform that allows you to develop scalable and secure business solutions. Within the Azure ecosystem, artificial intelligence capabilities facilitate the creation of intelligent assistants, process automation, content analysis, natural language processing, and AI-powered applications for various business sectors.
The AI-200 course is aimed at developers, software engineers, cloud architects, and technical professionals involved in artificial intelligence projects. Its practical approach allows you to implement real solutions using Azure AI, integrate cognitive services, work with advanced AI models, and develop applications ready for modern business environments.
In addition, this course is official from Microsoft and eligible for FUNDAE subsidies for companies, facilitating training in one of the fastest-growing technology areas with the highest professional demand.
Course Overview
The AI-200 Course: Develop AI Cloud Solutions on Azure provides the knowledge and skills needed to design, develop, and deploy artificial intelligence solutions in the cloud using Microsoft Azure services. This official training allows you to gain practical insight into building intelligent applications by leveraging the advanced AI capabilities of the Microsoft ecosystem.
Throughout the course, students will learn to work with technologies such as Azure AI Services, Azure OpenAI Service, language models, natural language processing, computer vision, and intelligent agents. Development patterns, service integration, and best practices for creating scalable, secure solutions ready for business environments will also be covered.
The training is aimed at developing applications capable of automating processes, analyzing information, generating intelligent content, and improving decision-making through artificial intelligence. All this with a practical approach that allows you to apply the knowledge acquired in real digital transformation projects based on Azure 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 December 31, 2026. One-attempt exam available only in Virtual - Distance Learning format.
What is the Microsoft AI-200 certification used for in the professional environment?
The Microsoft AI-200 certification validates the skills needed to design, develop, and deploy artificial intelligence solutions using Microsoft Azure services. It is aimed at technical professionals involved in generative AI projects, intelligent automation, virtual assistants, content analysis, and the development of artificial intelligence-powered applications.
In the job market, this certification is especially relevant for roles such as AI Developer, cloud developer, software engineer, solutions architect, or artificial intelligence consultant. Companies increasingly demand professionals capable of integrating AI models, intelligent agents, and cognitive services into secure and scalable business applications.
Obtaining the AI-200 certification improves employability, strengthens technical specialization, and allows you to participate in digital transformation projects where artificial intelligence becomes a strategic component for optimizing processes, automating tasks, and generating value from data.
Professional Applications of Microsoft Azure AI
Microsoft Azure AI is Microsoft's suite of artificial intelligence services designed to develop advanced business solutions in the cloud. The platform allows you to incorporate generative AI capabilities, natural language processing, computer vision, document analysis, and intelligent agent creation into corporate applications.
In organizations, Azure AI is used to automate processes, improve customer service, analyze unstructured information, generate intelligent content, and develop applications capable of interacting with users through natural language. These capabilities allow you to accelerate operational processes, improve productivity, and facilitate data-driven decision-making.
The AI-200 course allows you to apply these technologies in real scenarios by developing intelligent solutions on Azure, facilitating the creation of modern, scalable business applications ready to leverage the potential of artificial intelligence in various industries.
What the Official Microsoft Course at Nanfor Includes
The training includes Microsoft Learn material, presentations by expert instructors, authorized labs, specialized tutoring, and certification preparation.
Nanfor combines Microsoft's official content with expert guidance, allowing for training focused on practical application in real business environments.
Learn all the components
Advantages of the AI-200 Training
Official Microsoft artificial intelligence training: Updated content based on the most advanced Azure AI services.
Specialization in developing cloud-based AI solutions: 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 focused on 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 in the IT market.
Prerequisites
To make the most of this course, the following is recommended:
- Basic programming knowledge
- Familiarity with Azure concepts and cloud services
- General knowledge of artificial intelligence or data analysis (recommended, not required)
Preparation for the Microsoft Azure AI Cloud Developer Associate Certification
This AI‑200 course prepares you for the official Microsoft certification in developing artificial intelligence solutions on Azure (Microsoft Certified: Azure AI Cloud Developer Associate), providing the knowledge needed to design, build, and optimize cloud-based AI applications.
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Course duration:
100 hours
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Classroom access:
3 months
General course information
Who is this course aimed at?
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 want to move into AI Developer roles
Training objectives: What will you learn?
Upon completion of the AI‑200 course, the participant will be able to:
- Develop artificial intelligence solutions on Microsoft Azure
- Use Azure AI Services and Azure OpenAI in real projects
- Implement natural language processing (NLP) solutions
- Create computer vision and image analysis applications
- Design scalable AI architectures in the cloud
- Integrate artificial intelligence into business applications
Elements of the Microsoft Learn AI-200 collection
- 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: Develop artificial intelligence solutions in the cloud on Azure - Program
Unit 1: Implement container 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 the 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 resolve incidents
- 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-based scaling with KEDA
- Select appropriate compute resources
- Apply revision modes to control scaling and traffic
Lab / Practice:
- Configuring auto-scaling 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 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
- Run similarity queries
- Implement hybrid search
- Use the 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
- Run similarity searches
- Create vector indexes (IVFFlat, HNSW)
- Design retrieval patterns for RAG
Lab / Practice:
- Implementing vector search
Module 3: Optimize vector search
Learning objectives:
- Tune pgvector performance
- Optimize indexing strategies
- Scale PostgreSQL for AI workloads
Unit 6: Improve 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
- Choose between queues and topics
- Design messages for AI
- Process messages reliably with DLQ
Lab / Practice:
- Processing messages with Service Bus
Module 2: Develop workflows with Event Grid
Learning objectives:
- Design event-driven architectures
- Use the CloudEvents schema
- Configure event filtering and routing
- Manage retries and delivery
Lab / Practice:
- Publishing and receiving events
Module 3: Build serverless backends with Azure Functions
Learning objectives:
- Evaluate hosting options
- Create triggers and bindings
- Integrate Key Vault and App Configuration
- Apply security with managed identity
Unit 8: Manage secrets and configuration in AI solutions
Module 1: Manage secrets with Azure Key Vault
Learning objectives:
- Store secrets, keys, and certificates
- Retrieve secrets using SDKs
- Implement secure rotation
- Apply caching strategies
Lab / Practice:
- Managing secrets with Azure Key Vault
Module 2: Manage configuration with Azure App Configuration
Learning objectives:
- Connect applications to App Configuration
- Manage configuration using tags
- Implement feature flags
- Integrate with Azure Key Vault
Lab / Practice:
- Retrieving configuration and secrets
Unit 9: Monitor and troubleshoot Azure applications
Module 1: Instrument applications with OpenTelemetry
Learning objectives:
- Understand observability concepts
- Instrument applications with OpenTelemetry
- Create custom traces and spans
- Export telemetry to Application Insights
Lab / Practice:
- Instrumenting applications
Module 2: Analyze telemetry with logs and metrics
Learning objectives:
- Write KQL queries
- Analyze logs and metrics
- Create dashboards and workbooks
- Configure alerts
Lab / Practice:
Our differentiator: Hands-on labs
| Nanfor Lab |
Technical skills developed |
Practical learning outcome |
| Configuring the Azure environment for AI |
Creating Azure resources, managing subscriptions and AI services |
The student deploys a complete environment for developing cloud-based AI solutions |
| 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 functionality 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) |
Designing databases, 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 |
Optimizing performance and reducing latency |
The student improves the performance of AI applications in production |
| Integrating Azure AI services |
Using language, vision, voice, and content APIs |
The student integrates multiple AI capabilities into a single solution |
| Security and secret management |
Using Azure Key Vault, identities, and access |
The student protects applications and sensitive 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 |
Selecting services, cloud-native design |
The student designs robust, production-oriented solutions |
| Developing an end-to-end AI solution |
Integrating all the above components |
The student builds a complete application ready for an enterprise environment |
Language
- Course: English / Spanish
- Labs: English / Spanish
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 page. If you have any questions, please contact us.
If you want to take it in person or remotely please contact us:
Nanfor, Microsoft's official IT training center
Nanfor is a custom IT training center specializing in technology training for professionals and businesses, and it is officially accredited by Microsoft as:
- Microsoft Solutions Partner – Training Services
- Microsoft Cloud Partner
These accreditations certify that Nanfor meets Microsoft's standards for delivering technical courses, using Microsoft content and Microsoft Certified Trainers (MCTs), ensuring quality, continuous updates, and alignment with certifications.
Frequently Asked Questions
What is the AI‑200 course?
It is an official Microsoft training course that teaches how to develop cloud-based artificial intelligence solutions with Azure, using services such as Azure AI and Azure OpenAI.
What will I learn in this course?
You will learn how 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 (subject to the terms 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 delivered?
It is delivered online, with access to content, labs, and expert support, allowing you to progress flexibly.
Can it be subsidized through FUNDAE?
Yes. This course may be subsidized through FUNDAE, subject to the company's terms.
How long is the course access?
The course includes 3 months of access, with the option to extend it (except for subsidized training).
