Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The significance of AI in contemporary cluster management
- Constraints of conventional scaling and scheduling methodologies
- Essential ML concepts for resource governance
Core Principles of Kubernetes Resource Management
- Fundamentals of CPU, GPU, and memory allocation
- Navigating quotas, limits, and resource requests
- Detecting performance bottlenecks and inefficiencies
Machine Learning Strategies for Workload Scheduling
- Supervised and unsupervised models for optimizing workload placement
- Predictive algorithms for anticipating resource demand
- Incorporating ML features into custom schedulers
Reinforcement Learning for Smart Autoscaling
- How RL agents adapt based on cluster behavior
- Crafting reward functions for operational efficiency
- Constructing RL-driven autoscaling frameworks
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for accurate forecasting
- Integrating time-series models into autoscaling processes
- Assessing prediction precision and model calibration
Deploying AI-Driven Optimization Solutions
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-assisted decision making
Strategies for Cost and Performance Enhancement
- Lowering compute expenses through predictive scaling
- Boosting GPU efficiency via ML-based placement
- Striking a balance between latency, throughput, and overall efficiency
Practical Scenarios and Real-World Applications
- Utilizing AI to autoscale high-load applications
- Optimizing heterogeneous node pools
- Applying ML in multi-tenant environments
Conclusions and Future Directions
Requirements
- Solid grasp of Kubernetes core principles
- Hands-on experience with deploying containerized applications
- Working knowledge of cluster administration and resource management
Target Audience
- SREs managing extensive distributed systems
- Kubernetes operators handling 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
