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Production Deployment

Cloud Platform Deployment​

  • What you Need to Know

    • AWS AI Application Deployment

    • Google Cloud Platform AI Deployment

    • Microsoft Azure AI Deployment

Containerization and Orchestration​

  • What you Need to Know

    • Docker for AI Applications

    • Kubernetes for AI Workloads

      • Deploying AI applications on Kubernetes clusters
      • Resource management and GPU scheduling
      • Horizontal Pod Autoscaling for AI services
      • Resources:

    • Service Mesh for AI Applications

      • Istio for microservices communication
      • Traffic management and load balancing
      • Security and observability in service mesh
      • Resources:

Model Serving and Inference Optimization​

  • What you Need to Know

    • Model Serving Frameworks

      • TensorFlow Serving for TensorFlow models
      • TorchServe for PyTorch model deployment
      • ONNX Runtime for cross-framework inference
      • Resources:

    • Inference Optimization Techniques

      • Model quantization and pruning for faster inference
      • Batch processing and dynamic batching
      • Caching strategies for repeated requests
      • Resources:

    • Edge Deployment and Mobile Optimization

      • TensorFlow Lite for mobile and edge devices
      • Core ML for iOS deployment
      • ONNX.js for browser-based inference
      • Resources:

Scalability and Performance​

  • What you Need to Know

    • Auto-Scaling Strategies

    • Load Balancing and Traffic Management

      • Application load balancers for AI services
      • Traffic splitting for A/B testing
      • Circuit breakers and retry mechanisms
      • Resources:

    • Caching and Performance Optimization

Monitoring and Observability​

  • What you Need to Know

    • Application Performance Monitoring (APM)

      • Request tracing and latency monitoring
      • Error tracking and alerting systems
      • Resource utilization monitoring
      • Resources:

    • ML Model Monitoring

      • Model performance drift detection
      • Data quality monitoring and validation
      • Prediction accuracy tracking over time
      • Resources:

    • Logging and Error Tracking

      • Centralized logging with ELK stack
      • Structured logging for AI applications
      • Error aggregation and notification systems
      • Resources:

Security and Compliance​

  • What you Need to Know

    • AI Application Security

    • Data Privacy and Protection

    • Audit Logging and Compliance

DevOps and CI/CD for AI​

  • What you Need to Know

    • Continuous Integration for AI Applications

      • Automated testing pipelines for AI code
      • Model validation and performance testing
      • Integration testing with external AI services
      • Resources:

    • Continuous Deployment Strategies

    • Infrastructure as Code (IaC)

      • Terraform for cloud infrastructure provisioning
      • Ansible for configuration management
      • GitOps workflows for infrastructure deployment
      • Resources:

Cost Optimization and Resource Management​

Disaster Recovery and Business Continuity​

  • What you Need to Know

    • Backup and Recovery Strategies

      • Model versioning and artifact backup
      • Database backup and point-in-time recovery
      • Cross-region replication and failover
      • Resources:

    • High Availability Architecture

Performance Testing and Optimization​

  • What you Need to Know

    • Load Testing for AI Applications

      • Simulating realistic user traffic patterns
      • Testing model inference under load
      • Identifying performance bottlenecks
      • Resources:

    • Benchmarking and Profiling

      • Model inference benchmarking
      • Application profiling and optimization
      • Resource utilization analysis
      • Resources:

Congratulations! You have completed the comprehensive AI Engineering learning path. You now possess the skills to build, deploy, and maintain production-ready AI applications. Continue your journey by staying current with emerging AI technologies, contributing to open-source projects, and building innovative AI solutions that transform user experiences!