Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The critical role of AI in modern cluster management
- Constraints of conventional scaling and scheduling methods
- Fundamental ML concepts applicable to resource management
Basics of Kubernetes Resource Management
- Core principles of CPU, GPU, and memory allocation
- Interpreting quotas, limits, and resource requests
- Diagnosing performance bottlenecks and operational inefficiencies
Machine Learning Strategies for Workload Scheduling
- Supervised and unsupervised learning models for workload placement
- Predictive algorithms for anticipating resource demand
- Incorporating ML features into custom scheduler implementations
Reinforcement Learning for Adaptive Autoscaling
- Mechanisms by which RL agents interpret cluster behavior
- Constructing reward functions to drive operational efficiency
- Developing autoscaling strategies guided by reinforcement learning
Predictive Autoscaling Leveraging Telemetry Data
- Leveraging Prometheus data for accurate forecasting
- Implementing time-series models for autoscaling decisions
- Assessing prediction accuracy and refining model parameters
Deployment of AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes control planes
- Implementing intelligent control loops for resource management
- Enhancing KEDA capabilities for AI-assisted decision-making
Strategies for Cost and Performance Optimization
- Cutting compute expenses through predictive scaling techniques
- Boosting GPU efficiency via ML-driven resource placement
- Optimizing the balance between latency, throughput, and efficiency
Real-World Applications and Practical Scenarios
- Managing high-load application autoscaling with AI
- Optimizing resource distribution across heterogeneous node pools
- Applying ML techniques in multi-tenant deployment environments
Conclusion and Future Directions
Requirements
- Solid grasp of core Kubernetes principles
- Practical experience in deploying containerized applications
- Knowledge of cluster operations and resource management workflows
Target Audience
- SREs maintaining large-scale distributed systems
- Kubernetes operators overseeing high-volume 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
