Đề cương khóa học

Introduction to AI-Enhanced Kubernetes Operations

  • Why AI matters for modern cluster operations
  • Limitations of traditional scaling and scheduling logic
  • Key concepts of ML for resource management

Foundations of Kubernetes Resource Management

  • CPU, GPU, and memory allocation fundamentals
  • 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
  • Using 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

Yêu cầu

  • An understanding of Kubernetes fundamentals
  • Experience with containerized application deployments
  • Familiarity with cluster operations and resource management

Audience

  • SREs working with large-scale distributed systems
  • Kubernetes operators managing high-demand workloads
  • Platform engineers optimizing compute infrastructure
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