Course Outline

Introduction to AI-Enhanced Kubernetes Operations for Government

  • The Importance of AI in Modern Cluster Operations
  • Limitations of Traditional Scaling and Scheduling Methods
  • Key Concepts of Machine Learning for Resource Management

Foundations of Kubernetes Resource Management

  • Fundamentals of CPU, GPU, and Memory Allocation
  • Understanding Quotas, Limits, and Requests
  • Identifying Bottlenecks and Inefficiencies

Machine Learning Approaches for Scheduling

  • Supervised and Unsupervised Models for Workload Placement
  • Predictive Algorithms for Resource Demand
  • Utilizing ML Features in Custom Schedulers

Reinforcement Learning for Intelligent Autoscaling

  • How RL Agents Learn from Cluster Behavior
  • Designing Reward Functions for Efficiency
  • Building RL-Driven Autoscaling Strategies

Predictive Autoscaling with Metrics and Telemetry

  • Using Prometheus Data for Forecasting
  • Applying Time-Series Models to Autoscaling
  • Evaluating Prediction Accuracy and Tuning Models

Implementing AI-Driven Optimization Tools

  • Integrating ML Frameworks with Kubernetes Controllers
  • Deploying Intelligent Control Loops
  • Extending KEDA for AI-Assisted Decision-Making

Cost and Performance Optimization Strategies

  • Reducing Compute Costs Through Predictive Scaling
  • Improving GPU Utilization with ML-Driven Placement
  • Balancing Latency, Throughput, and Efficiency

Practical Scenarios and Real-World Use Cases

  • Autoscaling High-Load Applications with AI
  • Optimizing Heterogeneous Node Pools
  • Applying ML to Multi-Tenant Environments

Summary and Next Steps for Government

Requirements

  • A solid understanding of Kubernetes fundamentals
  • Practical experience with deploying containerized applications
  • Knowledge of cluster operations and resource management

Audience for Government

  • Site Reliability Engineers (SREs) working with large-scale distributed systems
  • Kubernetes operators responsible for managing high-demand workloads
  • Platform engineers focused on optimizing compute infrastructure
 21 Hours

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