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 contemporary cluster operations
- Constraints of conventional scaling and scheduling logic
- Core ML concepts applicable to resource management
Basics of Kubernetes Resource Management
- Fundamentals of CPU, GPU, and memory allocation
- Comprehending quotas, limits, and requests
- Recognizing performance bottlenecks and inefficiencies
Machine Learning Techniques for Scheduling
- Supervised and unsupervised models for workload placement
- Predictive algorithms for anticipating resource demand
- Integrating ML features into custom schedulers
Reinforcement Learning for Smart Autoscaling
- The process by which RL agents adapt to cluster behavior
- Constructing reward functions for efficiency
- Developing autoscaling strategies driven by RL
Forecast-Based Autoscaling Using Metrics and Telemetry
- Leveraging Prometheus data for forecasting
- Applying time-series models to autoscaling
- Assessing prediction precision and adjusting models
Implementing AI-Powered Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Deploying intelligent control loops
- Enhancing KEDA for AI-assisted decision-making
Strategies for Cost and Performance Optimization
- Lowering compute costs via predictive scaling
- Boosting GPU utilization through ML-driven placement
- Striking a balance between latency, throughput, and efficiency
Real-World Applications and Practical Scenarios
- Autoscaling high-load applications using AI
- Optimizing heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Conclusion and Future Steps
Requirements
- A solid grasp of Kubernetes core concepts
- Practical experience in deploying containerized applications
- 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 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
