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Duration 21 hours
Course Outline
Introduction to AI-Augmented Kubernetes Operations
- The significance of AI in contemporary cluster management
- Constraints of conventional scaling and scheduling logic
- Core ML principles applied to resource management
Basics of Kubernetes Resource Management
- Fundamentals of CPU, GPU, and memory allocation
- Interpreting quotas, limits, and resource requests
- Detecting bottlenecks and operational inefficiencies
ML Strategies for Workload Scheduling
- Applying supervised and unsupervised models for workload placement
- Predictive algorithms for estimating resource demand
- Incorporating ML features into custom scheduler implementations
Reinforcement Learning for Smart Autoscaling
- Mechanisms by which RL agents learn from cluster dynamics
- Designing reward functions to drive efficiency
- Constructing RL-powered autoscaling strategies
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for predictive insights
- Implementing time-series models in autoscaling logic
- Assessing prediction accuracy and model tuning
Deploying AI-Powered Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-assisted decision processes
Strategies for Cost and Performance Optimization
- Cutting compute costs via predictive scaling techniques
- Boosting GPU utilization through ML-based placement
- Optimizing the balance between latency, throughput, and efficiency
Real-World Scenarios and Practical Applications
- Managing AI-driven autoscaling for high-load applications
- Optimizing configurations in heterogeneous node pools
- Applying ML solutions in multi-tenant environments
Conclusion and Future Directions
Requirements
- Solid grasp of core Kubernetes concepts
- Proven experience deploying containerized applications
- Proficiency in cluster operations and resource management
Target Audience
- SREs maintaining large-scale distributed systems
- Kubernetes operators handling high-demand workloads
- Platform engineers focused on compute infrastructure optimization
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
