Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The significance of AI in modern cluster operations
- Constraints of conventional scaling and scheduling logic
- Core concepts of ML in resource management
Foundations of Kubernetes Resource Management
- Basics of CPU, GPU, and memory allocation
- Comprehending quotas, limits, and requests
- Detecting bottlenecks and inefficiencies
Machine Learning Approaches for Scheduling
- 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 learn from cluster behavior
- Crafting reward functions for efficiency
- Developing RL-driven autoscaling strategies
Predictive Autoscaling with Metrics and Telemetry
- Utilizing Prometheus data for forecasting
- Applying time-series models to autoscaling
- Assessing prediction accuracy and tuning models
Implementing AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Deploying intelligent control loops
- Expanding KEDA for AI-assisted decision-making
Cost and Performance Optimization Strategies
- Lowering compute costs through predictive scaling
- Enhancing GPU utilization with ML-driven placement
- Balancing latency, throughput, and efficiency
Practical Scenarios and Real-World Use Cases
- Autoscaling high-load applications with AI
- Optimizing heterogeneous node pools
- Applying ML to multi-tenant environments
Summary and Next Steps
Requirements
- A solid understanding of Kubernetes fundamentals.
- Experience with deploying containerized applications.
- Familiarity with cluster operations and resource management.
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
