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 importance of AI in modern cluster operations
- Constraints of conventional scaling and scheduling logic
- Fundamental ML concepts for resource management
Basics of Kubernetes Resource Management
- Core principles of CPU, GPU, and memory allocation
- Interpreting quotas, limits, and resource requests
- Recognizing bottlenecks and operational inefficiencies
Machine Learning Strategies 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 adapt by learning from cluster behavior
- Crafting reward functions to drive efficiency
- Constructing RL-driven autoscaling strategies
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for forecasting purposes
- Applying time-series models to autoscaling mechanisms
- Assessing prediction accuracy and fine-tuning models
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Establishing intelligent control loops
- Expanding KEDA capabilities for AI-assisted decision-making
Strategies for Cost and Performance Optimization
- Lowering compute expenses through predictive scaling
- Enhancing GPU utilization via ML-driven placement
- Striking a balance between latency, throughput, and efficiency
Practical Scenarios and Real-World Applications
- Autoscaling high-load applications using AI
- Optimizing performance in heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Summary and Path Forward
Requirements
- A solid grasp of Kubernetes core concepts
- Practical experience in deploying containerized applications
- Proficiency in 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
