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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The importance of AI in modern cluster operations
- Constraints of conventional scaling and scheduling logic
- Essential ML concepts for resource management
Core Principles of Kubernetes Resource Management
- Basics of CPU, GPU, and memory allocation
- Interpreting quotas, limits, and requests
- Detecting bottlenecks and operational inefficiencies
Applying Machine Learning to Scheduling
- Employing supervised and unsupervised models for workload placement
- Predictive algorithms for estimating resource demand
- Incorporating ML features into custom schedulers
Reinforcement Learning for Advanced Autoscaling
- Mechanisms by which RL agents learn from cluster dynamics
- Creating reward functions focused on efficiency
- Developing autoscaling strategies driven by RL
Forecasting Autoscaling via Metrics and Telemetry
- Utilizing Prometheus data for predictive purposes
- Implementing time-series models for autoscaling
- Assessing forecast accuracy and refining models
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Expanding KEDA capabilities for AI-assisted decisions
Strategies for Cost and Performance Optimization
- Lowering compute expenses via predictive scaling
- Enhancing GPU utilization through ML-based placement
- Striking a balance between latency, throughput, and efficiency
Practical Applications and Real-World Cases
- Autoscaling high-load applications with AI assistance
- Optimizing heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Conclusion and Future Directions
Requirements
- A solid grasp of Kubernetes core concepts
- Experience in deploying containerized applications
- Familiarity with cluster operations and resource management practices
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
