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🚀 Deploying AI at Scale: DeepSeek on Kubernetes! 🤖💡 The AI revolution is here, and DeepSeek-R1 is at the forefront of reasoning models. But how do you deploy, scale, and manage AI workloads efficiently? Kubernetes is the answer! 🔹 Why Kubernetes for DeepSeek? ✅ Scalability – Effortlessly scale workloads across multiple nodes ✅ Resilience – Auto-restart on failures, ensuring high availability ✅ Cloud & Local Flexibility – Deploy with KIND, Minikube, MicroK8s, or on cloud with Ingress ✅ Persistent Storage – Handle large AI models securely ✅ No GPU? No Problem! – Works even on CPU nodes (though slower) 🔹 What’s Inside? 🔹 End-to-end Kubernetes deployment guide for DeepSeek-R1 🔹 Ollama for inference + Open WebUI for interaction 🔹 YAML configurations to get started instantly ⚡ Whether you’re an AI enthusiast, DevOps engineer, or Kubernetes pro, this is your blueprint for running AI workloads at scale! Read the full guide 👉 [See below the Article Link] 💬 Excited about AI on Kubernetes? Let’s discuss in the comments! 🚀 #AI #Kubernetes #DeepSeek #MachineLearning #CloudComputing https://lnkd.in/gjwv9jTZ