
Master strategies for smoother Kubernetes upgrades through practical tips, best practices, and risk management techniques. Learn to handle multiple versioned components while minimizing disruption in your deployment process.

Explore strategies for managing stateful workloads across multiple Kubernetes environments, from edge computing to hybrid clouds, with focus on high availability, data management, and CSI features.

Discover essential security enhancements for Kubernetes that prevent escalation attacks through node-specific restrictions, bounded service account tokens, and validating admission policies for robust infrastructure protection.

Discover how to leverage live traffic inspection for automatic authorization in Envoy, using CNCF Pixie to generate OPA policies and implement effective service-to-service access controls.

Master OpenTelemetry implementation with hands-on exercises in Java and Python applications, covering instrumentation, data collection, and analysis using Jaeger and Prometheus for enhanced observability.

Discover how eBPF and Cilium enable efficient multicast networking in Kubernetes clusters, exploring implementation techniques, challenges, and solutions for streamlined pod-to-pod data delivery.

Explore essential strategies for implementing secure multi-tenant architectures in cloud-native platforms, focusing on micro-segmentation, GitOps automation, and policy-driven approaches for optimal workload isolation.

Master essential security practices for Kubernetes clusters through hands-on exercises covering authentication, authorization, network policies, and pod security to protect against potential threats.

Discover how Intuit modernized their Payroll Enterprise system using event-driven architecture, focusing on handling millions of transactions while ensuring scalability, security, and developer productivity with Numaflow.

Discover how to build a scalable network monitoring system using eBPF, Vector, and ClickHouse to process millions of events per second across Kubernetes clusters with real-world implementation insights.

Explore container security hardening through Seccomp and eBPF, comparing their approaches to limiting Linux kernel access and implementing the principle of least privilege in Kubernetes environments.

Discover how to effectively manage and distribute AI models using OCI standards and Harbor, covering model structures, security, replication, and acceleration mechanisms for enterprise deployment.

Explore advanced GPU scheduling in Kubernetes through reinforcement learning, focusing on optimizing multi-GPU setups, reducing resource fragmentation, and improving utilization for large-scale ML projects.

Explore GPU sharing strategies in Kubernetes using Dynamic Resource Allocation (DRA), comparing Multi-Instance GPUs, MPS, and CUDA Time-Slicing for optimal workload performance.

Discover how to optimize Kubernetes scheduling by building custom plugins with WebAssembly and testing at scale using KWOK, focusing on performance metrics and efficient cluster management.

Discover how Toyota Connected streamlined operations by implementing CNCF tools like ArgoCD, Backstage, and Harbor, improving collaboration and reducing time to market through standardized practices.

Discover how to optimize GPU utilization for LLM inference on Kubernetes through load-aware fractioning, using MIG slices to improve resource efficiency while maintaining performance standards.

Discover how intelligent apiserver proxying and workspace concepts enhance Kubernetes multi-tenancy, enabling better cluster-scoped operations and simplified RBAC management for improved service offerings.

Discover practical solutions for deploying and scaling LLMs in production using cloud-native technologies, focusing on KServe implementation, optimization techniques, and operational best practices.

Discover how to protect cloud applications from quantum computing threats by implementing quantum-safe cryptography in Kubernetes environments, with practical steps for securing data against future quantum attacks.

Discover how to streamline AI model training on Kubernetes using Kubeflow TrainJob API and JobSet, enabling efficient distributed training and LLM fine-tuning with simplified Python workflows.