Azure ML and Azure OpenAI Training in Astana, Kazakhstan

The Azure ML and Azure OpenAI Training is a hands-on program designed to teach how to build, train, deploy, and operate machine learning and Generative AI solutions using Microsoft Azure Machine Learning and Azure OpenAI Service.

The course covers end-to-end ML workflows, LLM integration, MLOps, security, and production deployment, enabling teams to create scalable, secure, and enterprise-grade AI applications on Azure.

Introduction to Azure ML & Azure OpenAI

• Azure AI ecosystem overview
• Azure ML vs Azure OpenAI roles
• Typical enterprise AI architectures
• Use cases for ML + Generative AI

Azure ML Workspace & Environment Setup

• Azure ML workspace components
• Compute instances and clusters
• Data stores and datasets
• Access control and networking basics

Data Preparation & Feature Engineering

• Data ingestion and preprocessing
• Feature engineering basics
• Data versioning and lineage
• Data quality considerations

Model Training with Azure ML

• Training scripts and experiments
• Hyperparameter tuning
• Distributed training concepts
• Experiment tracking

Model Registry & Deployment

• Registering models
• Real-time and batch endpoints
• Model versioning
• Deployment strategies

Azure OpenAI Fundamentals

• Azure OpenAI service overview
• Models and capabilities
• Authentication and quotas
• Cost management basics

Building AI Applications with Azure OpenAI

• API integration patterns
• Prompt engineering for apps
• Structured outputs
• Error handling and retries

Retrieval-Augmented Generation (RAG) on Azure

• RAG architecture on Azure
• Azure AI Search integration
• Document ingestion pipelines
• Grounded response generation

AI Agents & Workflow Orchestration

• Agent concepts with Azure OpenAI
• Tool calling and function integration
• Multi-step AI workflows
• Human-in-the-loop patterns

MLOps & CI/CD for Azure ML

• Azure ML pipelines
• Model lifecycle automation
• CI/CD integration (GitHub Actions/Azure DevOps)
• Environment promotion (dev/test/prod)

Monitoring & Observability

• Model performance monitoring
• Data drift detection
• Logging and tracing for AI apps
• Alerting and troubleshooting

Security, Privacy & Compliance on Azure

• Identity and access management (Entra ID)
• Network security and private endpoints
• Data protection and encryption
• Compliance and governance

Cost Management & Optimization

• Compute cost optimization
• Azure OpenAI token usage management
• Caching and reuse
• Budgeting and alerts

Hands-on Exercises

Summary and Conclusion