Scale Kubernetes: Optimize Your Systems

Transform your Kubernetes infrastructure from reactive to intelligent with advanced resource optimization strategies that power today's most demanding ML and AI workloads. This Short Course was created to help Machine Learning and AI professionals accomplish systematic resource optimization in production Kubernetes environments. By completing this course, you'll master the critical skills to analyze resource utilization patterns, configure Horizontal Pod Autoscalers with precision, and implement cost-effective scaling strategies that maintain optimal performance under varying workloads. By the end of this course, you will be able to: • Analyze resource utilization metrics across pods and nodes to identify scaling opportunities • Configure and tune Horizontal Pod Autoscalers based on CPU, memory, and custom metrics • Implement resource requests and limits that prevent contention while optimizing costs This course is unique because it combines real-world production scenarios with hands-on dashboard analysis and HPA tuning exercises that mirror the challenges faced by ML infrastructure teams managing GPU-intensive workloads. To be successful in this project, you should have a background in basic Kubernetes concepts, container orchestration, and system monitoring

• Continuously monitor and adjust scaling thresholds based on usage
• Balance performance and cost with 70-80% resource utilization
• Automate scaling considering startup times and traffic patterns
• Set resource requests and limits to ensure reliability

• Basic familiarity with concepts and environment
• Readiness for hands-on practice and exercises