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 Kubernetes
https://youtu.be/_jqMRb6Mt-0

6️⃣ Part 6: Auto-Scaling with HPA and Monitoring with Prometheus
https://youtu.be/ZG6BopWtvtE

7️⃣ Part 7: Troubleshooting Tips - Testing, Debugging & Optimization
https://youtu.be/3IGqEX-1ZhM

📥 Resources:
📌 GitHub - https://github.com/Abonia1/kubernetes-ml-project
📌 Medium - https://medium.com/@abonia/deploying-a-scalable-machine-learning-service-on-kubernetes-c3a137f6b527
📌 Docker hub link(if want to use existing image) - https://hub.docker.com/repository/docker/abonia/ml-tutorial/general
📌 Podman Installation Guide: https://podman.io/docs/installation
📌 Docker Desktop Download: https://www.docker.com/products/docker-desktop/
📌 Kind (Kubernetes in Docker) Installation: https://kind.sigs.k8s.io/docs/user/quick-start/
📌 Kubernetes Documentation: https://kubernetes.io/docs/home/
📌 Prometheus: https://prometheus.io/docs/introduction/overview/
📌 FastAPI Documentation: https://fastapi.tiangolo.com/#example-upgrade
📌 Scikit-Learn: https://scikit-learn.org/stable/supervised_learning.html

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🔗 Linkedin: https://www.linkedin.com/in/aboniasojasingarayar/
🔗 Find me on Github: https://github.com/Abonia1
🔗 Medium Articles: https://medium.com/@abonia

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