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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The significance of AI in modern cluster management
- The constraints of conventional scaling and scheduling approaches
- Core ML principles for resource administration
Foundations of Kubernetes Resource Management
- Basics of CPU, GPU, and memory provisioning
- Interpreting quotas, limits, and resource requests
- Detecting performance bottlenecks and inefficiencies
Machine Learning Strategies for Scheduling
- Employing supervised and unsupervised models for workload placement
- Predictive algorithms for estimating resource demand
- Incorporating ML features into custom schedulers
Reinforcement Learning for Intelligent Autoscaling
- How RL agents adapt based on cluster dynamics
- Crafting reward functions to drive efficiency
- Constructing autoscaling strategies guided by RL
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for future-state forecasting
- Implementing time-series models for autoscaling logic
- Assessing prediction accuracy and refining models
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-assisted decision processes
Cost and Performance Optimization Tactics
- Lowering compute expenses through predictive scaling
- Boosting GPU efficiency via ML-driven placement
- Striking a balance between latency, throughput, and efficiency
Practical Scenarios and Real-World Applications
- Autoscaling high-demand applications with AI assistance
- Optimizing heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Summary and Next Steps
Requirements
- A solid grasp of Kubernetes core concepts
- Practical experience in deploying containerized applications
- Competence in cluster operations and resource administration
Target Audience
- SREs managing large-scale distributed systems
- Kubernetes operators overseeing 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
