AI & Machine Learning Labs

Master multi-agent orchestration, production deployment, and enterprise AI system integration through advanced exam-level labs.

GenAI Expert Labs - Module 8

Multi-agent systems, deployment, and enterprise integration.

Lab 22: Multi-Agent Orchestration
Multi-Agent / Expert
Scenario: AI Research Team Coordination
TechCorp needs a multi-agent system for automated market research. Design a team of specialized AI agents: a Researcher for data gathering, an Analyst for insights, a Writer for reports, and a Coordinator for orchestration. Configure agent roles, communication protocols, task delegation, and conflict resolution to produce comprehensive market analysis reports.

Learning Objectives:

  • Agent Roles: Define specialized agent capabilities and responsibilities
  • Communication: Configure inter-agent messaging and protocols
  • Task Delegation: Set up hierarchical task distribution
  • Conflict Resolution: Handle disagreements and error recovery

Multi-Agent Configuration

Configure all agents
System Requirements
• 4 specialized agents required
• Hierarchical coordination
• Async communication enabled
• Shared memory workspace
• Error recovery mandatory
• Output: Structured report
Agent 1: Coordinator (Orchestrator)
Agent 2: Researcher (Data Gathering)
Agent 3: Analyst (Data Analysis)
Agent 4: Writer (Report Generation)
Communication & Memory
Progress: 0/18 settings configured
Score: 0/100
0%

Lab Completed!

Excellent multi-agent design!

Lab 23: LLM Deployment & Scaling
MLOps / Expert
Scenario: Production LLM Infrastructure
FinanceAI needs to deploy their fine-tuned LLM to production handling 10,000 requests/minute with 99.9% uptime. Configure infrastructure including compute resources, auto-scaling, load balancing, caching, monitoring, and cost optimization to meet SLA requirements within a $50,000/month budget.

Learning Objectives:

  • Infrastructure: Configure GPU instances, memory, and networking
  • Scaling: Set up auto-scaling policies and load balancing
  • Optimization: Implement caching, batching, and quantization
  • Monitoring: Configure observability and alerting

Deployment Configuration

Configure infrastructure
Production Requirements
• Throughput: 10,000 req/min
• Latency: p99 < 500ms
• Uptime SLA: 99.9%
• Budget: $50,000/month
• Model: 7B parameter LLM
• Region: Multi-region required
Compute Resources
Scaling & Load Balancing
Performance Optimization
Monitoring & Alerts
Progress: 0/18 settings configured
Score: 0/100
0%

Lab Completed!

Excellent deployment architecture!

Lab 24: AI System Integration
Integration / Expert
Scenario: Enterprise AI Platform Integration
GlobalBank needs to integrate their new AI assistant into existing systems: CRM (Salesforce), ticketing (ServiceNow), knowledge base (Confluence), and core banking APIs. Design the integration architecture including API design, authentication, data flow, error handling, and compliance requirements for handling sensitive financial data.

Learning Objectives:

  • API Design: RESTful endpoints, versioning, rate limiting
  • Authentication: OAuth 2.0, API keys, service accounts
  • Data Flow: ETL pipelines, real-time sync, webhooks
  • Compliance: Data encryption, audit logging, GDPR/PCI

Integration Architecture

Configure integrations
Compliance Requirements
• PCI-DSS compliant
• GDPR data handling
• SOC 2 audit logging
• Encryption at rest & transit
• Data residency: US/EU
• 7-year audit retention
API Design
Authentication & Security
System Integrations
Data & Compliance
Progress: 0/18 settings configured
Score: 0/100
0%

Lab Completed!

Excellent integration design!

Lab 22: Multi-Agent Orchestration Instructions

Objective

Design a multi-agent system with 4 specialized agents (Coordinator, Researcher, Analyst, Writer) for automated market research. Configure all agent roles, communication protocols, and orchestration settings.

Configuration Steps

  1. Coordinator Agent: Use GPT-4o for best reasoning. Select hierarchical delegation and DAG planning.
  2. Researcher Agent: Configure with Tavily search and multi-source data gathering.
  3. Analyst Agent: Use GPT-4o with code interpreter for data analysis.
  4. Writer Agent: Configure for executive summary or technical report output.
  5. Communication: Enable async messaging, shared vector DB memory, and coordinator-decides conflict resolution.
Pro Tips

Use async communication for parallel agent execution. Hierarchical delegation ensures clear task ownership. Vector DB shared memory allows agents to build on each other's work.

Recommended Settings
  • Coordinator: GPT-4o + hierarchical + DAG
  • Researcher: GPT-4o-mini + Tavily + multi-source
  • Analyst: GPT-4o + both tools + JSON output
  • Writer: GPT-4o + markdown + executive style
Requirements

All 4 agents must be configured. Error recovery is mandatory. Shared memory must be enabled for agent collaboration.

Lab 23: LLM Deployment Instructions

Objective

Deploy a 7B parameter LLM to production handling 10,000 req/min with 99.9% uptime and p99 latency under 500ms, within a $50,000/month budget.

Configuration Steps

  1. Compute: Select A100 40GB GPUs. Set min 4, max 16 instances for scaling headroom.
  2. Scaling: Use GPU utilization metric with 70% scale-up threshold.
  3. Optimization: Enable FP16 quantization, continuous batching, and paged attention (vLLM).
  4. Caching: Use semantic caching to reduce redundant inference.
  5. Monitoring: Set p99 latency alert at 500ms and error rate at 0.1%.
  6. Multi-region: Deploy to 2+ regions for 99.9% uptime SLA.
Pro Tips

vLLM with paged attention provides 2-4x throughput improvement. Continuous batching maximizes GPU utilization. FP16 halves memory with minimal quality loss.

Cost Calculation
  • A100 40GB: $3.50/hr × 8 instances × 720hr = $20,160/month
  • With auto-scaling: ~$25,000-35,000/month
  • Multi-region adds ~20% overhead
SLA Requirements

99.9% uptime requires multi-region deployment. p99 < 500ms requires optimized inference engine and caching. Monitor closely to stay under budget.

Lab 24: AI Integration Instructions

Objective

Design enterprise AI integration architecture connecting to CRM, ticketing, knowledge base, and core banking systems while meeting PCI-DSS and GDPR compliance requirements.

Configuration Steps

  1. API Design: Use REST with URL versioning and appropriate rate limiting.
  2. Authentication: OAuth 2.0 with short token expiry (15-60 min) and TLS 1.3 + AES-256.
  3. CRM Integration: Webhooks for real-time updates from Salesforce.
  4. Knowledge Base: RAG pipeline with vector embeddings for Confluence content.
  5. Banking API: Service mesh or API gateway with mTLS for security.
  6. Compliance: Tokenize PII, comprehensive audit logging, 7-year retention, explicit consent.
Pro Tips

Circuit breaker pattern prevents cascade failures. RAG pipeline enables semantic search over knowledge base. Tokenization is preferred over masking for PCI compliance.

Compliance Checklist
  • PCI-DSS: Tokenize card data, encrypt at rest
  • GDPR: Explicit consent, data residency (EU)
  • SOC 2: Comprehensive audit logging
  • Retention: 7 years for financial data
Critical Requirements

Banking API integration requires highest security (service mesh + mTLS). Never store PII in plain text. Always use explicit consent for GDPR compliance.