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Duration 21 hours
Course Outline
Foundations of AI-Enhanced Kubernetes Operations
- The strategic importance of AI in modern cluster management.
- Critiques of traditional scaling and scheduling paradigms.
- Core ML concepts applicable to resource management.
Kubernetes Resource Management Essentials
- Basics of CPU, GPU, and memory allocation.
- Interpreting quotas, limits, and resource requests.
- Diagnosing performance bottlenecks and inefficiencies.
Machine Learning Strategies for Scheduling
- Supervised and unsupervised models for workload placement.
- Predictive algorithms for anticipating resource demand.
- Incorporating ML features into custom scheduler development.
Reinforcement Learning for Adaptive Autoscaling
- Mechanisms for RL agents to learn cluster dynamics.
- Crafting reward functions to maximize efficiency.
- Developing robust RL-driven autoscaling strategies.
Predictive Autoscaling via Metrics and Telemetry
- Utilizing Prometheus data for accurate forecasting.
- Applying time-series models to autoscaling workflows.
- Assessing prediction accuracy and model calibration.
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with native Kubernetes controllers.
- Implementing intelligent control loops.
- Enhancing KEDA for AI-assisted decision-making.
Optimizing Cost and Performance
- Cutting compute costs through predictive scaling techniques.
- Boosting GPU utilization via ML-driven placement strategies.
- Striking a balance between latency, throughput, and efficiency.
Real-World Scenarios and Case Studies
- Managing high-load application autoscaling with AI.
- Optimizing heterogeneous node pool configurations.
- Applying ML principles to multi-tenant environments.
Conclusion and Forward-Looking Strategies
Requirements
- Strong foundational knowledge of Kubernetes.
- Proficiency in deploying containerized applications.
- Working knowledge of cluster operations and resource governance.
Target Audience
- SREs managing large-scale 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
