Part 4 | Hands-On End-to-End ML Model Deployment on Kubernetes | Containerize with Docker/Podman скачать в хорошем качестве

Part 4 | Hands-On End-to-End ML Model Deployment on Kubernetes | Containerize with Docker/Podman

In this tutorial, we'll be deploying a machine learning service on Kubernetes, encompassing: Sentiment Analysis Model: Developed using Scikit-Learn. FastAPI-based REST API: For seamless model inference. Containerization: Using Docker or Podman. Kubernetes Deployment: Featuring auto-scaling with Horizontal Pod Autoscaler (HPA). Persistent Storage: Ensuring reliable management of model artifacts. Monitoring: Implemented with Prometheus for real-time insights. This comprehensive guide is tailored for beginners eager to enhance their MLOps skills and gain practical experience in deploying machine learning applications in real-world scenarios. 💁🏻‍♀️ What You’ll Learn ▸ Developing a Sentiment Analysis Model using Scikit-Learn. ▸ Building a REST API with FastAPI for model inference. ▸ Containerizing Applications using Docker or Podman. ▸ Deploying on Kubernetes with configurations for auto-scaling. ▸ Setting Up Persistent Storage for model artifacts. ▸ Integrating Prometheus for monitoring and performance tracking. 👩🏻‍💻 Technical stack Scikit-Learn FastAPI Docker Podman Kubernetes Kind Prometheus Horizontal Pod Autoscaler (HPA) ⭐️ Topics Covered ⭐️ Introduction & Project Overview Setting Up Podman & Kind for Kubernetes Creating a Kubernetes Cluster Deploying Persistent Storage Setting Up ConfigMap for Configuration Management Deploying the ML Application on Kubernetes Exposing the Service & Auto-Scaling with HPA Setting Up Prometheus for Monitoring Testing the API & Prometheus Metrics Debugging Common Issues & Troubleshooting Conclusion & Next Steps 1️⃣ Part 1: Introduction & Project Setup    • Part 1 | Hands-On End-to-End ML Model Depl...   2️⃣ Part 2: Setup Podman and install Kind    • Part 2 | Hands-On End-to-End ML Model Depl...   3️⃣ Part 3: Building the Machine Learning Model & API    • Part 3 | Hands-On End-to-End ML Model Depl...   4️⃣ Part 4: Containerization with Docker/Podman    • Part 4 | Hands-On End-to-End ML Model Depl...   5️⃣ Part 5: Setting Up Kubernetes Cluster and Deploying the ML Service on Kubernetes    • Part 5 | Deploy ML Model on Kubernetes | S...   6️⃣ Part 6: Auto-Scaling with HPA and Monitoring with Prometheus    • Part 6 | Deploy ML Model on Kubernetes | A...   7️⃣ Part 7: Troubleshooting Tips - Testing, Debugging & Optimization    • Part 7 | Deploy ML Model on Kubernetes | S...   📥 Resources: 📌 GitHub - https://github.com/Abonia1/kubernetes... 📌 Medium -   / deploying-a-scalable-machine-learning-serv...   📌 Docker hub link(if want to use existing image) - https://hub.docker.com/repository/doc... 📌 Podman Installation Guide: https://podman.io/docs/installation 📌 Docker Desktop Download: https://www.docker.com/products/docke... 📌 Kind (Kubernetes in Docker) Installation: https://kind.sigs.k8s.io/docs/user/qu... 📌 Kubernetes Documentation: https://kubernetes.io/docs/home/ 📌 Prometheus: https://prometheus.io/docs/introducti... 📌 FastAPI Documentation: https://fastapi.tiangolo.com/#example... 📌 Scikit-Learn: https://scikit-learn.org/stable/super... ___________________________________________________________________________ 🔔 Get our Newsletter and Featured Articles: https://abonia1.github.io/newsletter/ 🔗 Linkedin:   / aboniasojasingarayar   🔗 Find me on Github: https://github.com/Abonia1 🔗 Medium Articles:   / abonia