Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Azure OpenAI Service Development in Western Sahara
Master Azure OpenAI Service Development with practical training in Generative AI, large language models (LLMs), prompt engineering, and AI application integration. Learn to build intelligent chatbots, automate business workflows, implement secure API integrations, and develop AI-powered solutions using Microsoft Azure. Gain hands-on experience with enterprise AI capabilities and responsible AI practices. Enhance your technical skills through industry-focused Azure OpenAI training in Western Sahara, and prepare for emerging AI development opportunities.
40 Hours/5 daysIntermediate4.4 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysIntermediate
Module 1: Introduction to Azure OpenAI Service
- Overview of Azure OpenAI Service and Generative AI.
- Key capabilities and enterprise use cases.
- Understanding large language models (LLMs) and their applications.
- Introduction to Azure OpenAI architecture and responsible AI principles in Western Sahara.
Module 2: Microsoft Azure Environment Setup
- Introduction to Azure subscriptions, resource groups, and Azure AI resources.
- Understanding model availability, access requirements, and deployment prerequisites.
- Configuring development environments and required tools.
- Managing access permissions and resource settings.
Module 3: Azure OpenAI API Fundamentals
- Understanding REST APIs, HTTP requests, and JSON responses.
- Connecting applications to Azure OpenAI endpoints.
- Authentication, API keys, and Microsoft Entra ID concepts.
- Handling API requests, responses, and common errors.
Module 4: Python Integration with Azure OpenAI
- Setting up Python and the required SDK packages.
- Connecting Python applications to Azure OpenAI deployments.
- Sending requests and processing model responses.
- Creating reusable functions for AI application development.
Module 5: Working with AI Models and Deployments
- Understanding supported model capabilities and deployment options.
- Configuring model parameters and request settings.
- Managing token limits and response lengths.
- Selecting appropriate models for application requirements.
Module 6: Prompt Engineering and Message Design
- Writing clear and effective prompts.
- Structuring system instructions and user messages.
- Using context and examples to guide model responses.
- Evaluating and refining prompts for consistent results.
Module 7: Building Conversational AI Applications
- Developing chatbot and virtual assistant workflows.
- Managing conversation history and context.
- Implementing multi-turn conversations.
- Designing user-friendly conversational experiences.
Module 8: Structured Outputs and Data Processing
- Generating structured responses for application workflows.
- Extracting relevant information from unstructured text.
- Working with JSON data and output validation.
- Applying classification and summarisation techniques.
Module 9: Tool Calling and Function Integration
- Understanding tool-calling concepts and supported model capabilities.
- Defining application functions and tool schemas.
- Connecting model responses to application-side functions.
- Validating tool inputs and handling execution errors.
Module 10: Retrieval-Augmented Generation (RAG)
- Understanding RAG architecture and business applications.
- Preparing, chunking, and organising documents.
- Exploring embeddings, vector search, and retrieval concepts.
- Grounding model responses in relevant source information.
Module 11: Enterprise Data and Application Integration
- Connecting AI applications to approved data sources.
- Designing context-aware question-answering workflows.
- Integrating AI capabilities into existing application components.
- Applying data validation and access controls.
Module 12: Security, Governance, and Responsible AI
- Protecting API credentials and sensitive information.
- Understanding identity, access permissions, and secure configuration.
- Exploring content filtering and responsible AI considerations.
- Applying privacy, compliance, and human-review practices where appropriate.
Module 13: Error Handling, Cost, and Performance Management
- Handling API errors, timeouts, and rate limits.
- Understanding token consumption and usage monitoring.
- Improving request efficiency and response reliability.
- Testing application performance and troubleshooting issues.
Module 14: Testing, Deployment, and Monitoring
- Testing AI application functionality and response quality.
- Validating outputs against defined requirements.
- Preparing applications for deployment.
- Monitoring application behaviour and maintaining configurations.
Module 15: Hands-On Capstone Project
- Define a business problem suitable for an Azure OpenAI solution.
- Design and develop a Python-based AI application.
- Integrate prompts, API requests, and response validation.
- Test the application for functionality, security, and reliability.
- Present the completed project and document key implementation decisions.
Entry requirements and prerequisites
- Basic Programming Knowledge: Familiarity with programming concepts such as variables, functions, loops, and conditional statements is recommended.
- Python Fundamentals: Basic Python knowledge is helpful for developing and integrating AI-powered applications.
- API and REST Fundamentals: Understanding HTTP requests, REST APIs, JSON, and API authentication provides a useful foundation.
- Cloud Computing Basics: Familiarity with cloud concepts and Microsoft Azure services is beneficial but not mandatory.
- Artificial Intelligence Fundamentals: A basic understanding of AI, machine learning, Generative AI, and large language models (LLMs) is advantageous.
- Microsoft Azure Account: Access to an Azure subscription and the required Azure OpenAI resources may be needed for practical exercises, subject to service availability and permissions.
- Development Environment: Familiarity with Visual Studio Code, Python environments, package installation, and command-line tools is helpful.
- Security Awareness: Basic knowledge of API keys, environment variables, access permissions, and protecting sensitive information is recommended.
- Problem-Solving Skills: An interest in troubleshooting, application development, and building AI-driven business solutions will help learners progress.
Career value
Reasons to choose this course
Master Azure OpenAI Services
Understand how to integrate Azure-hosted AI models into modern business applications.
Develop Generative AI Skills
Learn to build intelligent solutions using large language models and generative AI capabilities.
Build AI-Powered Applications
Gain practical knowledge of creating chatbots, virtual assistants, and intelligent business tools.
Learn Prompt Engineering
Design effective prompts to improve the relevance and quality of AI-generated responses.
Integrate APIs Securely
Understand API authentication, access management, and secure integration practices.
Automate Business Workflows
Explore how AI can simplify repetitive tasks, document processing, and information retrieval.
Advantages
Turn learning into practical capability
Azure AI Resource Configuration
Understand the configuration of Azure AI resources, deployment settings, and model access requirements to establish a suitable development environment.
Model Deployment and Selection
Learn how model availability, deployment choices, and configuration settings influence application capabilities and implementation decisions.
AI Application Architecture
Explore architectural approaches for connecting application components, AI services, data sources, and backend systems in maintainable solutions.
Performance and Cost Awareness
Understand token usage, request patterns, response times, and consumption monitoring to make informed application design decisions.
Enterprise Governance and Access Control
Learn how identity management, permissions, network controls, and organisational policies contribute to secure cloud AI environments.
Application Lifecycle Management
Develop an understanding of testing, configuration management, version control, monitoring, and maintenance practices for AI-enabled applications.
Exam format & assessment
- Exam Level: Beginner to Intermediate – Azure OpenAI Developer
- Exam Type: Online computer-based assessment.
- Number of Questions: 40 questions.
- Exam Duration: 90 minutes.
- Passing Score: 70% (28 out of 40 questions).
- Question Format: Multiple-choice and scenario-based questions, with an optional practical assessment.
- Assessment Platform: StepMerit online assessment platform.
- Practical Assessment: Learners may complete a hands-on task involving API integration or basic AI application development.
- Certification: Eligible learners may receive a StepMerit course completion certificate after meeting the course and assessment requirements.
- Evaluation Criteria: Assessment evaluates conceptual understanding, practical application, problem-solving, and secure AI development practices.
- Preparation Guidelines: Review the course materials and practise the concepts covered during the training.
Bring your learning together
Course conclusion
The Azure OpenAI Service Development course equips learners with practical knowledge to build and integrate AI-powered applications using Microsoft Azure and Generative AI technologies. Through structured learning and hands-on exercises, participants explore API integration, Python development, prompt engineering, conversational AI, structured outputs, tool calling, and Retrieval-Augmented Generation (RAG). The course also introduces essential practices for application security, responsible AI, performance optimisation, and deployment. StepMerit's Azure OpenAI Service Development training in Western Sahara, helps learners strengthen their technical capabilities, work on practical projects, and prepare for emerging opportunities in AI application development, cloud engineering, and enterprise Generative AI solutions.
Target audience
- Azure Cloud Developers
- Python Developers
- AI and Machine Learning Professionals
- Software Engineers
- IT Professionals
- Generative AI Enthusiasts
- Cloud Architects and Solution Designers
- Data Engineers and Data Professionals
- Automation Engineers
- Students and Aspiring AI Developers
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific expertise
- Professional Training Experience: 5+ years of experience in technology training, software development, cloud platforms, and AI-related concepts.
- Course Training Hours: 120+ hours of training in Azure OpenAI Service Development, Generative AI, Python integration, API development, and AI application workflows.
- Candidates Trained: 180+ candidates, including software developers, cloud engineers, IT professionals, Python developers, and aspiring AI engineers.
- Azure OpenAI Service Expertise: Knowledge of integrating Azure OpenAI models into applications, configuring deployments, and developing AI-powered solutions.
- Python and API Development: Practical training in Python programming, REST APIs, JSON processing, authentication, response handling, and application integration.
- Prompt Engineering and LLMs: Guidance on prompt design, conversational AI, structured outputs, and effective use of large language models.
- Healthcare Use-Case Experience: Demonstrates how Azure OpenAI can support healthcare document summarisation, administrative enquiry handling, and retrieval of information from approved healthcare documents.
- Hands-On Training Methodology: Uses demonstrations, coding exercises, guided labs, assignments, and practical projects to reinforce learning.
- Security and Responsible AI: Covers API credential protection, access control, data privacy, response validation, and responsible AI implementation.
- Project Guidance and Assessment: Supports learners in developing AI application prototypes, troubleshooting integration issues, testing responses, and presenting project outcomes.
Documented learner outcomes
- Problem Statement – A healthcare organisation manages large volumes of patient information, appointment enquiries, discharge instructions, medical documentation, healthcare policies, and administrative support requests. Healthcare staff spend considerable time searching documents, summarising records, answering routine questions, and retrieving relevant information from multiple sources. The organisation needs an AI-powered application using Azure OpenAI Service to improve information retrieval, automate administrative tasks, and enhance operational efficiency while maintaining patient confidentiality, response accuracy, data security, and responsible AI practices.
- How We Overcame the Problem – Learners analysed healthcare workflows and identified suitable use cases for Azure OpenAI Service integration. They developed a Python-based application connected to an appropriately configured Azure OpenAI deployment, prepared approved healthcare documents, designed context-aware prompts, and implemented a retrieval-based workflow to provide relevant information. Learners applied input validation, response verification, error handling, secure credential management, and access controls to improve application reliability and protect sensitive healthcare information.
- Overall Implementation – Learners implemented a healthcare AI application workflow covering requirement identification → healthcare document collection → document preprocessing → text chunking → Azure environment configuration → Python application development → relevant information retrieval → prompt construction → Azure OpenAI API integration → response generation → output validation → security checks → testing → final response delivery. The implementation demonstrated how Azure OpenAI Service can support healthcare document summarisation, administrative enquiry handling, policy information retrieval, and context-based question answering.
- Healthcare Document Processing – Learners prepared approved sample healthcare documents, including patient information guides, discharge instructions, hospital policies, appointment procedures, and administrative guidelines. They organised and processed the content to make relevant information easier to retrieve and use within the AI application.
- AI-Powered Healthcare Information Assistance – Learners developed a prototype assistant that responds to routine administrative questions using Azure OpenAI Service and approved source information. The application can help staff locate relevant information about appointments, hospital procedures, service availability, and administrative processes without treating generated responses as independently verified medical advice.
- Patient Document Summarisation – Learners explored how Azure OpenAI models can summarise authorised sample documents into concise, readable formats. They designed prompts to preserve important details, avoid unsupported conclusions, and enable healthcare professionals to review summaries before using them in operational workflows.
- Context-Based Question Answering and RAG – Learners implemented a Retrieval-Augmented Generation (RAG) workflow that retrieves relevant passages from approved healthcare documents and supplies them as context for AI-generated responses. They demonstrated how grounding responses in source material can improve relevance and help users verify information against the original documents.
- Prompt Engineering Evidence – Learners developed prompts with clear instructions, relevant context, response constraints, and expected output formats. They tested different prompt designs to improve response consistency, reduce unsupported statements, and guide the application to acknowledge when the available information is insufficient.
- Azure OpenAI and Python Integration – Learners connected a Python application to an Azure OpenAI deployment, configured endpoint and authentication settings, submitted requests, processed model responses, and handled JSON data. They developed reusable functions for document summarisation, administrative question answering, and healthcare information processing.
- Structured Output and Information Extraction – Learners explored structured responses for extracting key information from approved sample documents, classifying administrative enquiries, identifying appointment-related details, and organising information into application-friendly formats. They implemented output validation to ensure responses met the expected structure before further processing.
- Error Handling and Application Reliability – Learners tested scenarios involving invalid inputs, authentication failures, unavailable deployments, API errors, timeouts, rate limits, and missing source information. They implemented exception handling, appropriate fallback responses, and validation procedures to improve the reliability of the healthcare AI prototype.
- Security and Responsible AI Implementation – Learners applied secure credential management, access control, data minimisation, and privacy-aware development practices. They explored the importance of using authorised data, restricting access to sensitive information, reviewing generated responses, and escalating clinical or high-risk questions to qualified healthcare professionals. Real patient information should only be used in appropriately authorised and secured environments.
- Practical Evidence of Learning – Learners produced Python source code, Azure OpenAI API integration examples, prompt templates, sample healthcare document summaries, document-retrieval demonstrations, context-grounded question-answering examples, structured outputs, test cases, error-handling implementations, security checklists, and project documentation demonstrating their practical development skills.
- Overall Learner Outcome – By completing the implementation, learners demonstrated the ability to develop AI-powered healthcare application prototypes using Azure OpenAI Service and Python, prepare approved documents, implement context-based information retrieval, design effective prompts, generate structured responses, handle API errors, evaluate response quality, and apply security and responsible AI practices. The project provides practical evidence of how Azure-hosted Generative AI can support healthcare administration and information access while maintaining appropriate privacy protections and human oversight.
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It involves building AI-powered applications using OpenAI models hosted through Microsoft Azure.
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