Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The role of AI in modern cluster management
- Constraints of conventional scaling and scheduling methods
- Fundamental concepts of ML in resource management
Core Principles of Kubernetes Resource Management
- Basics of CPU, GPU, and memory distribution
- Navigating quotas, limits, and resource requests
- Detecting performance bottlenecks and inefficiencies
ML Techniques for Optimizing Scheduling
- Employing supervised and unsupervised models for workload placement
- Predictive algorithms for anticipating resource demand
- Incorporating ML features into custom scheduler logic
Reinforcement Learning for Smart Autoscaling
- How RL agents interpret and learn from cluster dynamics
- Crafting reward functions focused on efficiency
- Developing autoscaling strategies driven by RL
Predictive Autoscaling Leveraging Metrics and Telemetry
- Utilizing Prometheus data for forecasting trends
- Applying time-series models to autoscaling decisions
- Assessing prediction accuracy and refining models
Deploying AI-Driven Optimization Solutions
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-supported decision-making
Strategies for Cost and Performance Improvement
- Cutting compute expenses through predictive scaling
- Boosting GPU utilization via ML-informed placement
- Striking a balance between latency, throughput, and efficiency
Practical Applications and Real-World Case Studies
- Scaling high-load applications using AI
- Optimizing configurations for heterogeneous node pools
- Applying ML principles in multi-tenant settings
Key Takeaways and Future Directions
Requirements
- A solid grasp of Kubernetes core principles
- Practical experience in deploying containerized applications
- Working knowledge of cluster operations and resource management
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
