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Duration 21 hours
Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The critical role of artificial intelligence in advancing modern cluster management
- Evaluating the constraints of conventional scaling and scheduling methodologies
- Core principles of machine learning applied to resource governance
Foundations of Kubernetes Resource Management
- Essentials of CPU, GPU, and memory distribution
- Comprehending resource quotas, hard limits, and initial requests
- Detecting operational bottlenecks and systemic inefficiencies
Machine Learning Approaches for Scheduling
- Leveraging supervised and unsupervised models for optimal workload placement
- Employing predictive algorithms to anticipate resource requirements
- Incorporating machine learning features into custom scheduling engines
Reinforcement Learning for Intelligent Autoscaling
- Mechanisms by which reinforcement learning agents adapt to cluster dynamics
- Structuring reward functions to maximize operational efficiency
- Developing autoscaling strategies driven by reinforcement learning models
Predictive Autoscaling with Metrics and Telemetry
- Harnessing Prometheus telemetry for accurate workload forecasting
- Implementing time-series models to refine autoscaling processes
- Validating prediction accuracy and calibrating model performance
Implementing AI-Driven Optimization Tools
- Integrating machine learning frameworks with native Kubernetes controllers
- Deploying intelligent feedback loops for autonomous management
- Expanding KEDA capabilities to support AI-assisted decision-making
Cost and Performance Optimization Strategies
- Mitigating compute expenditure through predictive scaling techniques
- Enhancing GPU utilization via machine learning-driven placement
- Optimizing the balance between latency, throughput, and overall efficiency
Practical Scenarios and Real-World Use Cases
- Managing high-load application scaling with artificial intelligence
- Optimizing performance across heterogeneous node pools
- Applying machine learning models in multi-tenant operational environments
Summary and Next Steps
Requirements
- A foundational understanding of Kubernetes core concepts
- Demonstrated experience with containerized application deployment
- Familiarity with cluster operations and resource management practices
Audience
- Sites Reliability Engineers (SREs) managing large-scale distributed systems
- Kubernetes operators responsible for high-demand workloads
- Platform engineers focused on optimizing compute infrastructure
Testimonials (1)
I like the Kubernetes part of the training as I had some tasks with K8s pods and ports and it was nice to get a better understanding now. Thank you!
