AI-200 Course: Develop AI Solutions on Microsoft Azure
The AI-200: Develop AI Cloud Solutions on Azure course is official Microsoft training aimed at professionals who wish to design, develop, and implement artificial intelligence solutions using Azure services. This training provides the knowledge necessary 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 for the development of 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-driven applications for various business sectors.
The AI-200 course is aimed at developers, software engineers, cloud architects, and technical professionals participating in artificial intelligence projects. Its practical approach allows for the implementation of real-world solutions using Azure AI, integrating cognitive services, working with advanced AI models, and developing applications prepared for modern business environments.
Furthermore, this course is official Microsoft training and is subsidizable through FUNDAE for companies, facilitating training in one of the technological areas with the highest growth and professional demand.
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 services. This official training allows students to acquire a practical perspective on how to build intelligent applications leveraging the advanced AI capabilities of the Microsoft ecosystem.
Throughout the course, the student 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, and business-ready solutions will also be addressed.
The training is oriented toward the development of applications capable of automating processes, analyzing information, generating intelligent content, and improving decision-making through artificial intelligence. All this is done with a practical focus that allows for the application of acquired knowledge in real digital transformation projects based on Azure AI.
Virtual course with certification exam included as a gift. Do not 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 for the Virtual - Tele-training modality. Not applicable to the Self-Learning modality.
What is the Microsoft AI-200 certification for in a professional environment?
The Microsoft AI-200 certification validates the competencies necessary to design, develop, and implement artificial intelligence solutions using Microsoft Azure services. It is aimed at technical professionals involved in projects involving generative AI, intelligent automation, virtual assistants, content analysis, and the development of AI-powered applications.
In the job market, this certification is especially relevant for roles such as AI Developer, cloud developer, software engineer, solutions architect, or consultant specializing in artificial intelligence. Companies increasingly demand professionals capable of integrating AI models, intelligent agents, and cognitive services into secure and scalable enterprise applications.
Obtaining the AI-200 certification improves employability, reinforces technical specialization, and allows participation 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 for the incorporation of generative AI capabilities, natural language processing, computer vision, document analysis, and the creation of intelligent agents 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 for the acceleration of operational processes, improved productivity, and the facilitation of data-driven decision-making.
The AI-200 course enables the application of these technologies in real-world scenarios through the development of intelligent solutions on Azure, facilitating the creation of modern, scalable enterprise applications prepared to leverage the potential of artificial intelligence in various sectors of activity.
What the official Microsoft course at Nanfor includes
The training includes Microsoft Learn material, presentations from expert tutors, authorized labs, specialized tutoring, and certification preparation.
Nanfor combines official Microsoft content with expert support, allowing for training oriented toward practical application in real business environments.
View all components
Advantages of AI-200 Training
Official Microsoft artificial intelligence training: Updated content based on the most advanced Azure AI services.
Specialization in AI cloud solution development: You will learn to design scalable intelligent applications on Azure.
Mastery of key technologies like Azure OpenAI and Azure AI Services: Working with language models, vision, text analysis, and automation.
Practical approach oriented toward real projects: Development of solutions applicable in enterprise environments.
High professional demand in AI and Cloud: Training aligned with one of the fastest-growing areas in the IT market.
Prerequisites
To get the most out of this course, it is recommended to have:
- 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 necessary to design, build, and optimize AI-based applications in the cloud.
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Course duration:
100 hours
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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 working with cloud solutions
- IT professionals interested in incorporating AI into their applications
- Technical profiles looking to transition into AI Developer roles
Training objectives: What will you learn?
Upon finishing 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 enterprise 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: Developing artificial intelligence cloud solutions on Azure - Program
Unit 1: Implementing container application hosting on Azure
Module 1: Storing and managing 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: Deploying containers to Azure App Service
Learning objectives:
- Deploy custom containers to Azure App Service
- Configure the container runtime environment (ports, startup, storage)
- Configure application settings and connection strings
- Monitor and troubleshoot containerized applications
Lab / Practice:
- Deploying a container to Azure App Service
Unit 2: Deploying and managing applications in Azure Container Apps
Module 1: Deploying containers in Azure Container Apps
Learning objectives:
- Understand Azure Container Apps environments
- Deploy via CLI and YAML files
- Configure environment variables and secrets
- Configure authentication with the container registry
- Verify deployments via logs and revisions
Lab / Practice:
- Deploying a containerized backend API
Module 2: Managing containers in Azure Container Apps
Learning objectives:
- Manage revisions and update images
- Diagnose revision errors
- Monitor logs and resolve issues
- Configure health probes
- Optimize resources and scaling
Lab / Practice:
- Diagnosing and resolving a failed deployment
Module 3: Scaling 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:
- Setting up auto-scaling with KEDA
Unit 3: Deploying and monitoring applications in Azure Kubernetes Service
Module 1: Deploying 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 to AKS
Module 2: Configuring 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: Monitoring and troubleshooting 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: Developing AI solutions with Azure Cosmos DB for NoSQL
Module 1: Querying Azure Cosmos DB for NoSQL
Learning objectives:
- Understand the Cosmos DB data model
- Perform CRUD operations with the SDK
- Choose between point reads and queries
- Construct SQL queries for NoSQL
Lab / Practice:
- Building a document store for RAG
Module 2: Implementing 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: Optimizing query performance
Learning objectives:
- Analyze query patterns and RU consumption
- Configure indexes (range, composite, vector)
- Optimize indexing policies
- Select appropriate consistency levels
Unit 5: Developing AI solutions with Azure Database for PostgreSQL
Module 1: Building and querying 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: Implementing 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:
- Vector search implementation
Module 3: Optimizing vector search
Learning objectives:
- Tune pgvector performance
- Optimize indexing strategies
- Scale PostgreSQL for AI workloads
Unit 6: Enhancing AI solutions with Azure Managed Redis
Module 1: Implementing 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:
- Implementation of data operations in Redis
Module 2: Implementing messaging with Redis
Learning objectives:
- Use pub/sub for real-time messaging
- Implement Streams as task queues
- Choose the right messaging pattern
- Design processing pipelines for AI
Lab / Practice:
- Event publishing and subscription
Module 3: Implementing vector storage in Redis
Learning objectives:
- Create vector indexes with RediSearch
- Store and query embeddings
- Develop semantic search applications
Unit 7: Integrating backend services for AI solutions
Module 1: Processing 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:
- Message processing with Service Bus
Module 2: Developing 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:
- Event publishing and receiving
Module 3: Building 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: Managing secrets and configuration in AI solutions
Module 1: Managing secrets with Azure Key Vault
Learning objectives:
- Store secrets, keys, and certificates
- Retrieve secrets via SDK
- Implement secure rotation
- Apply caching strategies
Lab / Practice:
- Secrets management with Azure Key Vault
Module 2: Managing configuration with Azure App Configuration
Learning objectives:
- Connect applications to App Configuration
- Manage configuration using labels
- Implement feature flags
- Integrate 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:
- Understand observability concepts
- Instrument applications with OpenTelemetry
- Create custom traces and spans
- Export telemetry to Application Insights
Lab / Practice:
- Application instrumentation
Module 2: Analyzing telemetry with logs and metrics
- Write KQL queries
- Analyze logs and metrics
- Create dashboards and workbooks
- Configure alerts
Lab / Practice:
Our differentiating factor: Hands-on labs
| Nanfor Lab |
Technical skills developed |
Practical learning outcome |
| Azure environment configuration for AI |
Creation of resources in Azure, management of subscriptions and AI services |
The student deploys a complete environment for developing AI solutions in the cloud |
| Development of applications with Azure OpenAI |
Integration of GPT models, embeddings, and response generation |
The student creates generative AI applications ready for real-world use |
| Implementation of RAG (Retrieval-Augmented Generation) solutions |
Indexing, embeddings, vector search, and data grounding |
The student builds systems that combine AI with corporate data |
| Creation of serverless APIs with Azure Functions |
API design, triggers, bindings, and serverless logic |
The student exposes AI functionality through scalable web services |
| Development of event-driven architecture |
Use of Event Grid, Service Bus, and messaging |
The student implements decoupled and scalable AI workflows |
| Deployment of applications with containers |
Use of Azure Container Apps and Azure Kubernetes Service (AKS) |
The student deploys AI solutions in modern cloud environments |
| Container image management |
Use of Azure Container Registry (ACR) |
The student manages versions and deployments of AI applications |
| Development of solutions with Cosmos DB (NoSQL) |
Database design, queries, and optimization |
The student manages data storage for AI applications |
| Use of PostgreSQL with pgvector |
Implementation of vector databases |
The student builds advanced semantic search engines |
| Implementation of caching with Azure Redis |
Performance optimization and latency reduction |
The student improves the performance of AI applications in production |
| Integration of Azure AI services |
Use of language, vision, speech, and content APIs |
The student integrates multiple AI capabilities into one solution |
| Security and secrets management |
Use of Azure Key Vault, identities, and access |
The student protects applications and sensitive data in AI environments |
| Observability and monitoring |
Use of logs, metrics, OpenTelemetry, and KQL |
The student monitors performance and detects errors in AI solutions |
| Deployment automation |
Configuration of basic DevOps pipelines |
The student automates the delivery of AI solutions |
| Design of scalable AI architectures |
Service selection, cloud-native design |
The student designs robust, production-oriented solutions |
| Development of end-to-end AI solution |
Integration of all previous components |
The student builds a complete application ready for the enterprise environment |
Language
- Course: English / Spanish
- Labs: English / Spanish
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 page. If you have any questions, please contact us.
If you want to take it in in-person or remote-classroom mode, please contact us:
Nanfor, official Microsoft IT training center
Nanfor is a customized IT training center, specialized 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 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 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 (subject to current promotion terms).
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 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).
