Course Outline
Introduction to AI-Augmented Kubernetes Operations
- The significance of AI in contemporary cluster management
- Constraints of conventional scaling and scheduling methodologies
- Core concepts of machine learning in resource management
Kubernetes Resource Management Fundamentals
- Basics of CPU, GPU, and memory allocation
- Comprehending quotas, limits, and resource requests
- Recognizing performance bottlenecks and inefficiencies
Machine Learning Strategies for Scheduling
- Supervised and unsupervised models for workload placement
- Predictive algorithms for estimating resource demand
- Incorporating ML features into custom scheduler configurations
Reinforcement Learning for Intelligent Autoscaling
- How reinforcement learning agents adapt based on cluster behavior
- Crafting reward functions to drive efficiency
- Developing autoscaling strategies driven by RL
Predictive Autoscaling Using Metrics and Telemetry
- Utilizing Prometheus data for forecast modeling
- Implementing time-series models to drive autoscaling
- Assessing prediction accuracy and optimizing model parameters
Deploying AI-Driven Optimization Tools
- Integrating ML frameworks with Kubernetes controllers
- Implementing intelligent control loops
- Enhancing KEDA for AI-assisted decision-making processes
Strategies for Cost and Performance Optimization
- Cutting compute costs through predictive scaling
- Boosting GPU utilization via ML-guided placement
- Striking a balance between latency, throughput, and overall efficiency
Real-World Scenarios and Practical Use Cases
- Scaling high-load applications using AI
- Optimizing configurations across heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Summary and Future Directions
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 optimizing compute infrastructure
Testimonials (4)
basic understanding of container/kubernetes and how they interact features of the openshift plattform
Eric Scholze - NOW IT GmbH
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How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
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The knowledge and exchanges with Augustin
