🚀 Deploying AI Models

Master the art of taking AI models from development to production at scale

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Deploying AI Models Curriculum

12
Deployment Units
~90
Deployment Concepts
20+
Platforms & Tools
35+
Production Patterns
1

Deployment Fundamentals

Understand the key concepts, challenges, and considerations for deploying AI models to production.

  • Development vs production
  • Deployment strategies
  • Infrastructure requirements
  • Performance considerations
  • Scalability planning
  • Security implications
  • Cost optimization
  • Deployment lifecycle
2

Model Serving Architectures

Learn different architectures and patterns for serving AI models in production environments.

  • Batch vs real-time serving
  • Model-as-a-Service
  • Microservices architecture
  • Serverless deployments
  • Edge computing
  • Hybrid approaches
  • Load balancing strategies
  • Fault tolerance
3

Containerization and Docker

Master containerization techniques for packaging and deploying AI models consistently.

  • Docker fundamentals
  • Dockerfile best practices
  • Multi-stage builds
  • Container optimization
  • GPU support
  • Registry management
  • Security scanning
  • Image versioning
4

Kubernetes for AI

Deploy and orchestrate AI models using Kubernetes for scalable production systems.

  • Kubernetes basics
  • AI workload patterns
  • Resource management
  • Auto-scaling
  • GPU scheduling
  • Service mesh
  • Operators for ML
  • Monitoring and logging
5

Cloud Deployment Platforms

Leverage cloud platforms for scalable and managed AI model deployment solutions.

  • AWS AI services
  • Google Cloud AI Platform
  • Azure Machine Learning
  • Serverless inference
  • Managed endpoints
  • Multi-cloud strategies
  • Cost optimization
  • Vendor comparison
6

Model Optimization

Optimize AI models for production deployment through quantization, pruning, and acceleration.

  • Model quantization
  • Pruning techniques
  • Knowledge distillation
  • ONNX optimization
  • TensorRT acceleration
  • Hardware-specific optimization
  • Benchmarking
  • Trade-off analysis
7

CI/CD for AI Models

Implement continuous integration and deployment pipelines for AI model lifecycles.

  • MLOps pipelines
  • Automated testing
  • Model validation
  • Deployment automation
  • Rollback strategies
  • A/B testing
  • Canary deployments
  • Blue-green deployment
8

Monitoring and Observability

Implement comprehensive monitoring and observability for production AI systems.

  • Performance monitoring
  • Model drift detection
  • Data quality monitoring
  • Infrastructure metrics
  • Alerting systems
  • Distributed tracing
  • Log aggregation
  • Health checks
9

Security and Compliance

Ensure security and regulatory compliance for AI model deployments.

  • Security best practices
  • Data privacy
  • Access control
  • Encryption
  • Vulnerability scanning
  • Compliance frameworks
  • Audit trails
  • Risk management
10

Edge and Mobile Deployment

Deploy AI models to edge devices and mobile platforms for local inference.

  • Edge computing concepts
  • Mobile deployment
  • TensorFlow Lite
  • Core ML
  • ONNX Runtime
  • Federated learning
  • Offline capabilities
  • Resource constraints
11

Performance and Scaling

Optimize performance and implement scaling strategies for high-traffic AI applications.

  • Performance profiling
  • Horizontal scaling
  • Vertical scaling
  • Caching strategies
  • Batch processing
  • GPU utilization
  • Load testing
  • Capacity planning
12

Advanced Deployment Patterns

Explore advanced deployment patterns and emerging technologies for AI model serving.

  • Multi-model serving
  • Model ensembles
  • Shadow deployments
  • Feature flags
  • Chaos engineering
  • Service mesh integration
  • Event-driven architectures
  • Future trends

Unit 1: Deployment Fundamentals

Understand the key concepts, challenges, and considerations for deploying AI models to production.

Development vs Production

Learn the critical differences between development and production environments for AI models.

Environment Requirements Constraints
Moving from development to production involves significant changes in requirements, constraints, and operational considerations. Understanding these differences is crucial for successful AI model deployment.
# Development vs Production Environment
environment_comparison = {
  "development": {
    "purpose": "Model experimentation and validation",
    "characteristics": {
      "data": "Clean, curated datasets",
      "performance": "Focus on accuracy and metrics",
      "infrastructure": "Flexible, researcher-friendly",
      "timeline": "Iterative, experimental",
      "monitoring": "Basic validation metrics"
    },
    "tools": ["Jupyter notebooks", "Local GPUs", "Small datasets", "Ad-hoc scripts"]
  },
  "production": {
    "purpose": "Reliable service delivery to end users",
    "characteristics": {
      "data": "Real-world, noisy, streaming data",
      "performance": "Latency, throughput, availability",
      "infrastructure": "Scalable, reliable, secure",
      "timeline": "24/7 operation",
      "monitoring": "Comprehensive observability"
    },
    "requirements": ["High availability", "Security", "Compliance", "Cost efficiency"]
  },
  "key_differences": {
    "scale": "Individual requests vs millions of users",
    "reliability": "Best effort vs SLA guarantees",
    "security": "Open access vs restricted, audited",
    "maintenance": "Manual vs automated operations"
  }
}

Deployment Strategies

Explore different strategies for deploying AI models to minimize risk and maximize reliability.

Common Deployment Strategies:
• Blue-Green: Two identical environments, switch traffic instantly
• Canary: Gradual rollout to subset of users
• Rolling: Sequential replacement of instances
• A/B Testing: Compare multiple model versions
• Shadow: Run new model alongside existing without serving traffic
Risk Mitigation:
Each deployment strategy offers different trade-offs between deployment speed, risk exposure, and resource requirements. Choose based on your risk tolerance and infrastructure capabilities.
# Deployment Strategy Patterns
deployment_strategies = {
  "blue_green": {
    "description": "Two identical production environments",
    "process": [
      "Deploy new model to green environment",
      "Test green environment thoroughly",
      "Switch load balancer to green",
      "Keep blue as fallback"
    ],
    "advantages": ["Instant rollback", "Zero downtime", "Full testing"],
    "disadvantages": ["Double infrastructure cost", "Complex data consistency"]
  },
  "canary": {
    "description": "Gradual rollout to increasing user percentages",
    "process": [
      "Deploy to 5% of traffic",
      "Monitor metrics and errors",
      "Gradually increase to 25%, 50%, 100%",
      "Rollback if issues detected"
    ],
    "advantages": ["Risk limitation", "Real user feedback", "Performance validation"],
    "disadvantages": ["Slower rollout", "Complex monitoring", "Partial user experience"]
  },
  "shadow": {
    "description": "New model processes requests but doesn't serve responses",
    "use_cases": ["Performance testing", "Behavior analysis", "Safety validation"],
    "advantages": ["Zero user impact", "Real workload testing"],
    "disadvantages": ["Additional infrastructure", "Complex comparison analysis"]
  }
}

Infrastructure Requirements

Understand the infrastructure components and requirements for AI model deployment.

Core Infrastructure Components:
• Compute resources: CPUs, GPUs, memory, storage
• Networking: Load balancers, CDNs, API gateways
• Storage: Model artifacts, data, logs, metrics
• Orchestration: Container platforms, schedulers
• Monitoring: Metrics, logs, traces, alerts