AI, Machine Learning, Generative AI & Azure AI Foundry
Duration
10 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
Overview
This 10-day advanced, implementation-focused program delivers a structured journey from Machine Learning foundations to enterprise-grade Generative AI and multi-agent architectures using Microsoft Azure AI ecosystem services. Participants will design ML pipelines, build LLM-powered applications using Azure OpenAI Service, orchestrate intelligent agents through Azure AI Foundry, and implement Retrieval-Augmented Generation (RAG), multi-tool agents, and evaluation workflows.
The program emphasizes production-ready architectures, enterprise orchestration patterns, governance, scalability, cost optimization, and AI solution lifecycle management. By the end of the course, learners will be capable of engineering secure, scalable, and observable AI systems aligned with enterprise deployment standards.
Audience
- AI / ML Engineers moving into Generative AI, RAG, and Agent system development
- Cloud Engineers working on AI workloads in Microsoft Azure ecosystem
- Generative AI Engineers building LLM-powered enterprise applications (experience with ecosystems like OpenAI is helpful)
- Solution Architects designing enterprise AI, multi-agent, and RAG architectures
- Data Scientists transitioning from model building to production AI system deployment
- Backend / Platform Engineers moving into AI application and agent orchestration development
- Technical Product Managers working on AI platforms (with strong technical understanding)
- Mid-level to Senior engineers working on cloud, APIs, and distributed systems
Prerequisites
- Basic programming experience (preferably Python)
- Familiarity with cloud services (Azure preferred)
- Working Azure subscription with OpenAI and AI Foundry access
- Access to OpenAI API key
- Visual Studio Code with Python and Azure extensions
- Basic understanding of APIs
Curriculum
Module 1: Artificial Intelligence & ML Fundamentals
- Evolution of Artificial Intelligence
- AI vs Machine Learning vs Deep Learning
- Types of AI workloads in enterprises
- Supervised, Unsupervised, Reinforcement Learning
- Classification vs Regression
- Data → Model → Evaluation lifecycle
- Model evaluation metrics (Accuracy, Precision, Recall)
Module 2: Hands-on ML Implementation
- Environment setup (Python, Anaconda, Jupyter)
- Exploring structured datasets (IRIS dataset)
- Feature selection basics
- Training a supervised ML model
- Model evaluation and interpretation
Module 3: Generative AI Core Concepts
- What is Generative AI?
- Transformer architecture overview
- Prompt engineering fundamentals
- Tokens, temperature, and response control
- Embeddings and semantic similarity
- Responsible AI principles
Module 4: Azure OpenAI Deployment & Usage
- Overview of Azure OpenAI Service
- Resource provisioning in Azure
- Model deployment process
- Chat Completions API
- REST vs SDK integration patterns
- Hands-on prompt experimentation
Module 5: Azure AI Architecture Fundamentals
- Overview of Azure AI Foundry
- Integration between Azure OpenAI and Foundry
- Resource orchestration patterns
- Identity & RBAC configuration
- Networking and private endpoints
- Cost governance fundamentals
Module 6: Foundry SDK & Workflow Setup
- SDK vs REST approach
- Model routing and orchestration
- Environment configuration
- Observability basics
Module 7: Azure AI Agents Fundamentals
- What are AI Agents?
- Agent lifecycle and execution flow
- Threads and contextual memory
- Grounding concepts
- Foundry SDK vs Assistants comparison
Module 8: Building Agents with SDK
- Creating an agent using Foundry UI
- Developing SDK-based agents
- Bing Search grounding integration
- Testing and validating agent workflows
Module 9: Function Calling Architecture
- Tool invocation lifecycle
- Enterprise use cases
- Designing structured function schemas
- Error handling strategies
- Security considerations
Module 10: OpenAPI & External Integrations
- OpenAPI schema fundamentals
- Integrating OpenWeather API
- Rate limiting and resilience patterns
- Testing enterprise API-based agents
Module 11: RAG Architecture Design
- RAG fundamentals
- Naïve vs Hybrid RAG
- Embeddings pipeline design
- Chunking strategies
- Context window optimization
Module 12: Vector Search Implementation
- Vector indexing with Azure Cognitive Search
- Hybrid search design
- Latency vs accuracy trade-offs
- Building a RAG-powered agent
Module 13: Multi-Tool Agents
- Designing multi-tool agents
- Tool prioritization logic
- Code Interpreter capabilities
- Execution orchestration
Module 14: Multi-Channel Pipelines (MCP)
- MCP architecture overview
- Cross-platform AI orchestration
- Local MCP server setup
- Building a Weather MCP server
Module 15: Semantic Kernel Architecture
- Overview of Semantic Kernel SDK
- Memory and planning concepts
- Plugin types (Prompt, Native, Planner)
Module 16: Plugin Engineering
- Building prompt template plugins
- Developing native plugins
- Implementing planner workflows
- Chaining agent tasks
Module 17: Multi-Agent System Design
- Centralized vs decentralized agents
- Agent communication strategies
- Supervisory vs collaborative orchestration
- Scalability considerations
Module 18: Advanced Agent Development
- Group chat agent implementation
- Plugin-integrated agents
- Distributed observability patterns
- Cost optimization in multi-agent systems
Module 19: Local Development & DevOps
- Azure AI Foundry Local setup
- CI/CD for AI applications
- Integration with LangChain
- Infrastructure as Code basics
Module 20: Evaluation & Optimization
- Evaluation SDK overview
- Prompt evaluation frameworks
- Tracing with Application Insights
- Token usage dashboards
- Cost optimization strategies
- Governance and Responsible AI monitoring
Duration
10 Days
Level
Advanced Level
Design and Tailor this course
As per your team needs
