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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The strategic importance of AI in modern cluster management
- Constraints of conventional scaling and scheduling methodologies
- Core ML concepts applicable to resource governance
Foundations of Kubernetes Resource Management
- Basics of CPU, GPU, and memory distribution
- Navigating quotas, limits, and resource requests
- Detecting performance bottlenecks and inefficiencies
Machine Learning Approaches for Scheduling
- Utilizing supervised and unsupervised models for optimal workload placement
- Forecasting resource demand with predictive algorithms
- Incorporating ML features into custom scheduler logic
Reinforcement Learning for Intelligent Autoscaling
- Mechanisms by which RL agents interpret cluster behavior
- Crafting reward functions for efficiency maximization
- Constructing autoscaling strategies driven by reinforcement learning
Predictive Autoscaling with Metrics and Telemetry
- Leveraging Prometheus data streams for forecasting
- Applying time-series models to autoscaling logic
- Assessing prediction precision and refining model parameters
Implementing AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Deploying intelligent feedback loops
- Extending KEDA capabilities for AI-assisted decision-making
Cost and Performance Optimization Strategies
- Lowering compute expenses via predictive scaling techniques
- Enhancing GPU utilization through ML-optimized placement
- Striking the optimal balance between latency, throughput, and efficiency
Practical Scenarios and Real-World Use Cases
- Scaling high-load applications using AI insights
- Optimizing performance across heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Summary and Next Steps
Requirements
- A solid grasp of Kubernetes core concepts
- Hands-on experience deploying containerized applications
- Proficiency in cluster operations and resource management practices
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
