
Discover how to apply DevOps practices to data operations using cloud native tools, implementing databases in Kubernetes, and managing data as code through GitOps methodologies and best practices.

Discover how Kubernetes Operators can automate database management and serve as virtual DBAs, handling deployment, monitoring, high availability, and upgrades in cloud-native environments.

Discover how to implement efficient, cost-effective stream data processing using Kubernetes-native technologies while ensuring resilience and scalability in cloud-native environments.

Discover how to implement Federated Machine Learning with Kubernetes for privacy-preserving model training across edge devices, enabling efficient image classification and scaled computations.

Discover how to achieve 50%+ power savings in edge data centers using K8s-compatible Workload Allocation Optimizer (WAO), featuring real-world implementation across 200+ servers and sustainable computing practices.

Discover how to optimize edge computing using Akri and WebAssembly for efficient IoT device management, data processing, and serverless applications in Kubernetes environments.

Discover how a global shipping company implemented Kubernetes on ocean vessels, addressing unique challenges of edge computing at sea while managing distributed clusters and intermittent connectivity.

Discover how OpenFeature revolutionizes QA processes through feature flagging, enabling streamlined testing, simplified environment management, and controlled deployments for faster, more reliable software releases.

Discover how eBay successfully implemented OpenFeature across their decision-making platform, focusing on developer experience, simplified onboarding, and scaling from zero to billions of operations.

Master troubleshooting techniques for the OpenTelemetry Operator in Kubernetes, covering installation challenges, auto-instrumentation issues, and collector deployment solutions for confident implementation.

Discover how policy-as-code bridges the gap between development and security teams, improving developer experience while maintaining robust system security. Learn implementation best practices and overcome common challenges.

Discover how The New York Times developed their Internal Developer Platform, focusing on user-centric design, feedback integration, and strategic feature rollout while balancing simplicity with customization.

Discover how Canada Life modernized their platform infrastructure, transitioning from on-premise to cloud-based solutions using Amazon EKS, addressing scalability, security, and observability challenges while enhancing user experience.

Discover how to build effective platform interfaces that enhance developer experience and operability while reducing maintenance overhead through strategic platform engineering principles.

Explore the evolution from no-code to full-code approaches in Kubernetes platform engineering, examining real-world lessons and impacts on developer experience, testing, and operational stability.

Discover how FICO leverages Crossplane and Dapr to build efficient automation pipelines, streamline DevOps workflows, and implement Anything-as-Code for enhanced deployment flexibility and velocity.

Discover how modern tech companies implement data science, AI, and platform engineering principles through real-world insights from SAS' senior leadership and development practices.

Master the art of Chaos Engineering through hands-on experiments and real-world examples. Learn to strategically introduce system failures, monitor responses, and build more resilient applications.

Master observability techniques for event-driven architectures, from understanding system events and messages to implementing effective tracing and metrics for better system monitoring and troubleshooting in distributed systems.

Master secure secret management by replacing .env files with Pulumi ESC, featuring end-to-end encryption, dynamic credential fetching, and seamless integration with AWS and Terraform for enhanced security and scalability.

Master the fundamentals of deploying and managing Large Language Models on Kubernetes, covering essential concepts from basic architecture to practical implementation strategies for training and serving.