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 https://youtu.be/hlntSaGY-dQ 2️⃣ Part 2: Setup Podman and install Kind https://youtu.be/sKWZY0GJSuE 3️⃣ Part 3: Building the Machine Learning Model & API https://youtu.be/pc6GCL41BXk 4️⃣ Part 4: Containerization with Docker/Podman https://youtu.be/9mIu3DKJHhU 5️⃣ Part 5: Setting Up Kubernetes Cluster and Deploying the ML Service on Kube