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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The significance of AI in modern cluster management
- Constraints of conventional scaling and scheduling algorithms
- Fundamental ML concepts for resource management
Basics of Kubernetes Resource Management
- Core principles of CPU, GPU, and memory allocation
- Navigating quotas, limits, and resource requests
- Detecting performance bottlenecks and inefficiencies
ML Techniques for Workload Scheduling
- Supervised and unsupervised models for workload placement
- Predictive algorithms for assessing resource demand
- Integrating ML features into custom schedulers
Reinforcement Learning for Smart Autoscaling
- How RL agents adapt to cluster behavior patterns
- Crafting reward functions to drive efficiency
- Developing RL-based autoscaling strategies
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for forecasting purposes
- Applying time-series models to autoscaling processes
- Assessing prediction accuracy and refining models
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-assisted decision-making
Strategies for Cost and Performance Optimization
- Cutting compute costs via predictive scaling techniques
- Boosting GPU utilization through ML-driven placement
- Achieving balance between latency, throughput, and efficiency
Practical Scenarios and Industry Use Cases
- Managing high-load applications using AI autoscaling
- Optimizing heterogeneous node pools
- Applying ML strategies in multi-tenant environments
Conclusion and Future Directions
Requirements
- A solid grasp of Kubernetes core concepts
- Practical experience in deploying containerized applications
- Familiarity with cluster administration and resource management workflows
Target Audience
- SREs managing large-scale distributed systems
- Kubernetes operators overseeing high-demand workloads
- Platform engineers focused on optimizing compute infrastructure
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
basic understanding of container/kubernetes and how they interact features of the openshift plattform
