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Purpose:Set up an automated, scalable ML workflow using modern MLOps tools. Technologies Used: Key Tasks: Skills Applied: Result:A working ML deployment on Kubernetes and AWS, built for efficiency and repeatability. Link: Deploying Machine Learning Models with Kubeflow and KServe: A Comprehensive Guide | by Shijun Ju | Jun, 2025 | Medium Code: shj37/Minikube-Kubeflow-ML-KServe-AWS-Project All credit …
This project demonstrates the deployment of a machine learning model using Docker and Kubernetes on Google Cloud Platform (GCP), highlighting skills in MLOps, containerization, and cloud infrastructure management. The process integrates a pre-trained PyCaret model into a Flask application, containerized with Docker, and orchestrated with Kubernetes for scalability and reliability. Project Overview Key Steps Outcomes …