Скачать или смотреть Part 6 | Deploy ML Model on Kubernetes | Auto-Scaling with HPA and Monitoring with Prometheus

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...

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