
Explore vector search, retrieval-augmented generation, and scaling challenges. Learn from benchmarks, AI pre-training experiments, and serving billions of vectors. Discover optimal GPU choices and efficient search result serving methods.

Learn techniques for efficiently scaling GPU resources in Kubernetes clusters to optimize generative AI workloads, including GPU sharding and resource management strategies.

Explore multi-tenancy in MLOps: efficient resource sharing, isolation, and security for scaling ML operations. Learn implementation strategies using open-source tools like Flyte for cost-effective and secure multi-tenant environments.

Discover how to enhance Kafka consumer scalability using Kubernetes and KEDA, achieving over 75% efficiency while maintaining data freshness SLA. Learn about custom metrics, dynamic vertical scaling, and holistic optimization approaches.

Explore GitHub's scalable infrastructure using Kubernetes, including deployment processes, security measures, and self-service tools for efficient microservices management at massive scale.

Explore the evolution of platform engineering, from initial viable solutions to scalable architectures, focusing on cloud-native approaches and best practices.

Discover Lagoon, an open-source platform for seamless web application delivery. Learn its benefits for developers and non-developers, use cases, upcoming features, and why it's ideal for managing multiple web apps at scale without Kubernetes expertise.

Explore the challenges and complexities of decentralized systems, focusing on security implications, communication issues, and potential solutions in modern tech landscapes.

Uncover the realities of Kubernetes migration, from initial allure to Day-2 operations. Gain insights on overcoming challenges and leveraging tools like Komodor for a smoother transition.

Explore advanced techniques for managing Infrastructure as Code at scale using ArgoCD, Terraform, and Vault, enhancing cloud-native deployments and security.

Explore how load balancer services are expanding beyond public clouds, revolutionizing network traffic management and enhancing system performance across diverse computing environments.

Explore common pitfalls in microservice architecture and learn how to build scalable systems using event-driven design. Gain insights on avoiding distributed monoliths and creating truly independent services.

Explore best practices for durable messaging in distributed systems, addressing challenges and pitfalls while leveraging benefits like reliability, scalability, and decoupling.

Explore KEDA for Kubernetes event-driven autoscaling, optimizing resource utilization. Learn implementation, challenges, and cost-saving potential through a real-world use case and live demo.

Explore Karpenter, a Kubernetes-native cluster autoscaler, and learn to seamlessly migrate worker nodes and pods using Argo Workflows. Discover enhanced security and efficient management of large-scale node roll-overs.

Explore Databricks' implementation of Argo tools for dynamic scaling, metrics delivery, and overcoming cloud-hosted Kubernetes limitations in Model Serving workloads across multiple cloud providers.

Explore the power of Application Sets and matrix generators in Kubernetes, unlocking new dimensions of flexibility and scalability for managing thousands of applications simultaneously.

Explore how kcp enhances platform engineering by creating a global control plane for internal services, enabling a SaaS-like experience and transforming internal developer platforms into service marketplaces.

Explore automating deployment of large AI models on Kubernetes, leveraging CRDs for seamless management and optimizing GPU utilization through preset configurations and auto-provisioning.

Practical strategies for scaling Argo Workflows, covering infrastructure optimization, resource management, workflow design, and observability. Learn from real-world experience handling 18,000+ pods per workflow.

Explore how Argo Workflows enables scalable ML platforms across industries. Learn from experts about balancing high-scale operations with user-friendly interfaces for efficient ML team workflows.