#kubernetes (36)
- KubeVela Simplifies Kubernetes App Delivery for Platform Teams
KubeVela simplifies application delivery on Kubernetes for platform teams, enabling them to build a robust control plane using the Open Application Model.
- KubeVela: Simplify Kubernetes Application Delivery for Teams
Understand why KubeVela simplifies Kubernetes application delivery for platform teams, addressing complexity and improving operational efficiency.
- Deploy KubeVela and Learn the Open Application Model OAM
After this chapter, you will have KubeVela running in your cluster and a solid grasp of OAM's core concepts for application delivery.
- Your First Application: Defining Components with KubeVela
Learn how to define and deploy your first application component using KubeVela, understanding its abstraction over raw Kubernetes resources for simplified delivery.
- Adding Capabilities: Scaling and Exposing Applications with Traits
Learn how to add operational capabilities like scaling and exposing applications using KubeVela Traits, empowering developers while platform teams define reusable abstractions.
- Defining Your Delivery Pipeline: Mastering KubeVela Workflows
Explore KubeVela Workflows to define and automate complex application delivery pipelines on Kubernetes, integrating custom steps, policies, and multi-stage deployments.
- Governing Applications: Applying Policies for Compliance and Security
Discover how KubeVela Policies enable robust application governance, compliance, and security within your Kubernetes environment, reducing operational overhead for platform teams.
- Extend KubeVela with Definitions and Addons
Platform engineers can extend KubeVela's capabilities using custom definitions and addons, enhancing application delivery with CUE.
- KubeVela in Practice: Real-World Scenarios and Ecosystem Comparisons
Explore KubeVela's role in real-world application delivery scenarios and compare its capabilities with raw Kubernetes, Helm, Argo CD, and custom internal platforms.
- Streamline Kubernetes Application Delivery with KubeVela
Discover how KubeVela's core concepts, including OAM, streamline Kubernetes application deployment and empower platform teams to build robust internal developer platforms.
- How Kubernetes Controller Manager Works: Deep Dive into Internals
Deep technical explanation of how the Kubernetes Controller Manager works under the hood - architecture, internals, reconciliation loops, and real-world examples.
- How Linux Cgroups v1 and v2 Control System Resources
Grasp the architectural evolution from Linux cgroups v1 to v2 and their critical role in container resource isolation and stability.
- Lessons from Self-Hosting Kubernetes for a Docs Platform
Explore the technical journey of self-hosting a Kubernetes-based collaborative document platform, covering its architecture, implementation, and operational lessons learned.
- Kubernetes 1.36 Strengthens Security and Optimizes AI/ML Workloads
Learn how Kubernetes 1.36 enhances admission control security with new policies, optimizes AI/ML workloads, and provides deeper performance insights.
- Self-Hosting Trigger.dev: Taking Full Control (Advanced)
Explore the intricacies of self-hosting Trigger.dev, covering core architecture, local Docker Compose setup, and essential production deployment considerations for advanced users.
- Enhance Microservices with the Sidecar Pattern for Common Tasks
Implement the Sidecar Pattern to enhance microservices with auxiliary processes for logging, monitoring, and security, boosting operational efficiency.
- Deploy and Manage Large Language Models in Production
Learn to deploy, manage, and optimize Large Language Models in production, covering inference, scaling, monitoring, and cost-efficient LLMOps practices.
- Essential AI Infrastructure for LLM Serving
Explore the foundational AI infrastructure required for robust, scalable, and cost-efficient LLM serving, covering hardware, software, and architectural patterns.
- Dynamic Model Routing and A/B Testing for LLMs
Master dynamic model routing and A/B testing strategies for LLMs to optimize performance, cost, and user experience in production environments.
- Build & Optimize LLM Inference Pipelines for Production
You will learn to build, optimize, and scale robust LLM inference pipelines, mastering GPU optimization and effective scaling strategies for production.
- Build an End-to-End Production RAG System with LLMOps
Build a robust, scalable, and cost-efficient Retrieval Augmented Generation system using LLMOps best practices for real-world production.
- Mastering Cost Optimization for LLM Inference
Master techniques to identify LLM inference cost drivers and implement GPU optimization, smart caching, and dynamic scaling for cost-efficient production.
- Scale LLM Deployments from Single Instances to Clusters
Learn to scale Large Language Model deployments from single instances to robust, high-throughput clusters using Kubernetes and auto-scaling.
- Monitoring and Observability for Production LLM Systems
Master LLM monitoring and observability to track performance, manage costs, detect model drift, and ensure your production systems run reliably.
- Project: Deploying a Production-Ready Data Workflow
Learn how to deploy a production-ready data workflow using MetaDataHub, Docker, and Apache Airflow.
- Production Deployment & Scaling AI Agents
Learn how to deploy and scale AI agents in production using Docker and Kubernetes.
- DevOps Practices for Efficient Software Delivery and Operations
Learn to integrate development and operations using key DevOps tools and practices to build, deliver, and operate software more reliably and efficiently.
- Kubernetes Core Concepts - The Orchestra Conductor
Learn Kubernetes core concepts and how to manage containerized applications at scale.
- Advanced Kubernetes - Scaling, Configuration & Secrets
Learn how to scale applications automatically, manage configurations, and protect secrets in Kubernetes.
- Deploying a Multi-Service Application to Kubernetes
Successfully deploy complex multi-service applications to Kubernetes, mastering YAML configurations and ensuring robust inter-service communication.
- Learn Docker: Build & Deploy Production Applications Confidently
Learn Docker from basic concepts to advanced deployment, enabling you to confidently build, secure, and optimize containerized applications for production.
- Build & Manage Scalable Container Clusters with Docker Swarm
Configure and manage Docker Swarm clusters to deploy and scale containerized applications, ensuring high availability and efficient load balancing across hosts.
- What's Next? Beyond Docker Engine
Learn how to manage containerized applications at scale with Docker orchestration platforms like Kubernetes and Swarm.
- Troubleshooting and Debugging Docker
Learn how to effectively troubleshoot and debug Docker containers, images, networks, and volumes.
- Continuous Integration & Deployment Automation
Build a fully-automated CI/CD pipeline for Java web applications using Jenkins, Maven, and Docker.
- Deploy Apps with Helm & Kubernetes on Azure AKS
Readers will learn to deploy, manage, and scale containerized applications efficiently using Helm, Kubernetes, and Azure Kubernetes Service.