Course Outline
Introduction to AI-Enhanced Kubernetes Operations
- The importance of AI in modern cluster operations
- Constraints of traditional scaling and scheduling logic
- Core concepts of ML in resource management
Foundations of Kubernetes Resource Management
- Basics of CPU, GPU, and memory allocation
- Navigating quotas, limits, and resource requests
- Recognizing performance bottlenecks and inefficiencies
Machine Learning Strategies for Scheduling
- Employing supervised and unsupervised models for workload placement
- Predictive algorithms for anticipating resource demand
- Incorporating ML features into custom schedulers
Reinforcement Learning for Intelligent Autoscaling
- How RL agents derive insights from cluster behavior
- Formulating reward functions to maximize efficiency
- Constructing RL-driven autoscaling strategies
Predictive Autoscaling via Metrics and Telemetry
- Leveraging Prometheus data for forecasting
- Applying time-series models to autoscaling processes
- Assessing prediction accuracy and fine-tuning models
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 costs through predictive scaling
- Enhancing GPU utilization via ML-driven placement
- Striking a balance between latency, throughput, and efficiency
Practical Scenarios and Real-World Applications
- Scaling high-load applications using AI
- Optimizing heterogeneous node pools
- Applying ML techniques in multi-tenant environments
Summary and Next Steps
Requirements
- A solid grasp of Kubernetes fundamentals.
- Hands-on experience with 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 (3)
basic understanding of container/kubernetes and how they interact features of the openshift plattform
Eric Scholze - NOW IT GmbH
Course - Introduction to Containers, Kubernetes & OpenShift
About the microservices and how to maintenance kubernetes
Yufri Isnaini Rochmat Maulana - Bank Indonesia
Course - Advanced Platform Engineering: Scaling with Microservices and Kubernetes
The training met expectations with its clear explanations, real-world examples, and hands-on labs that made complex topics easy to understand. It provided valuable insights into container orchestration, security, scaling and many other advanced topics.
