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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The role of AI in modern cluster operations
- Constraints of conventional scaling and scheduling mechanisms
- Core ML concepts applicable to resource management
Foundations of Kubernetes Resource Management
- Basics of CPU, GPU, and memory allocation
- Interpreting quotas, limits, and resource requests
- Identifying performance 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 adapt based on cluster behavior
- Crafting reward functions for operational efficiency
- Developing RL-driven autoscaling strategies
Predictive Autoscaling with Metrics and Telemetry
- Leveraging Prometheus data for forecasting
- Applying time-series models to autoscaling processes
- Assessing prediction accuracy and model tuning
Implementing AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Deploying intelligent control loops
- Extending KEDA to support AI-assisted decision-making
Cost and Performance Optimization Strategies
- Lowering compute expenses via predictive scaling
- Enhancing GPU utilization through ML-driven placement
- Balancing latency, throughput, and overall efficiency
Practical Scenarios and Real-World Use Cases
- Autoscaling high-load applications using AI
- Optimizing heterogeneous node pools
- Applying ML in multi-tenant environments
Summary and Next Steps
Requirements
- A solid grasp of core Kubernetes concepts
- Hands-on experience deploying containerized applications
- Working knowledge of cluster management and resource oversight
Target Audience
- SREs responsible for 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
