
Insights on leveraging open-source tech for Apache Spark on Kubernetes, addressing challenges in large-scale production environments and evolving ML data infrastructure in the cloud.

Learn to customize Argo CD and Argo Rollouts for specific company needs while maintaining flexibility. Explore extension mechanisms and gain architectural insights for effective deployment workflows.

Discover strategies for implementing Argo Workflows across diverse engineering teams, overcoming adoption challenges, and customizing the platform for performance without sacrificing flexibility.

Optimize big data and ML workflows using memoization and work avoidance techniques in Argo Workflows. Learn best practices to reduce runtime and compute costs for data processing and machine learning jobs.

Learn to enhance security for Jupyter Notebooks using KubeArmor, exploring advanced protection techniques for cloud-native environments and data science workflows.

Discover best practices for building scalable Spark clusters on Kubernetes using Argo Workflows and Argo Events, enabling efficient resource utilization and seamless scalability for powerful data processing.

Step-by-step guide on configuring Cilium to limit GitHub repo access for cluster workloads, covering TLS termination, certificate management, and L7 network policies for enhanced security in multi-tenant environments.

Learn to schedule Jupyter notebooks using Airflow on Kubernetes, enabling scalable data pipelines and machine learning workflows with enhanced security and resource management.

Explore Rook, a cloud-native storage operator for Kubernetes, and its integration with Ceph for stable block, file system, and object storage in production environments.

Explore Apache Spark on Kubernetes using Charmed Spark. Learn to deploy, configure, and run Spark workloads, integrate with Kafka, and monitor jobs through hands-on demonstrations.

Explore a K8s-native Serverless Spark Service that enhances speed and simplicity, featuring a new K8s operator for cluster creation and job submission, with rapid responsiveness and ML training integration.

Explore user empathy in product development through a case study of Istio's Ambient mode, learning strategies for gathering honest feedback and understanding user perspectives.

Explore the journey of operationalizing data science workloads at scale using Kubeflow and GitOps, from initial challenges to a standardized approach for ML experiments, API creation, and batch predictions delivery.

Explore Kubeflow's strengths, weaknesses, and future developments as an MLOps platform based on user feedback and upcoming release plans.

Enhance Apache Spark's reliability on Kubernetes with Cloud Shuffle Manager, enabling fault tolerance and cost savings through Spot Instances while maintaining performance for large-scale data processing.

Explore how Pachama uses Flyte to manage complex data and ML pipelines for transparent forest carbon credit computation, including workload management, configuration, and lessons learned.

Explore Rook, a cloud-native storage operator for Kubernetes, and its integration with Ceph. Learn about block, file system, and object storage solutions for production environments.

Explore scalable stream processing on Kubernetes with Numaflow, an open-source framework enabling easy, cost-efficient data analytics for various engineering roles, powering large-scale operations at Intuit.

Learn how contributing to Kubernetes documentation can shape your career in open source. Discover impactful ways to contribute, hear from industry experts, and explore opportunities for growth in the cloud native ecosystem.

Explore best practices for building scalable AI/ML platforms using cloud-native technologies like Kubernetes, Kubeflow, and KServe. Learn to tackle challenges in MLOps and large model inference systems.

Explore PepsiCo's data science transformation using Kubeflow, addressing challenges in model reproducibility and enhancing efficiency, collaboration, and scalability in machine learning workflows.