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Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- Why AI is critical 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
Requirements
- A solid understanding of Kubernetes fundamentals
- Experience with containerized application deployments
- Familiarity with cluster operations and resource management
Audience
- SREs managing large-scale distributed systems
- Kubernetes operators handling high-demand workloads
- Platform engineers optimizing compute infrastructure
21 Hours
Testimonials (3)
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The knowledge and exchanges with Augustin
