Data Analyst, Data Scientist, Data Engineer, Educator
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 implements an automated MLOps pipeline for deploying a machine learning model, focusing on operational efficiency and security. It integrates Jenkins for CI/CD, Docker for containerization, and AWS ECS for scalable deployment, ensuring a repeatable and reliable workflow. Project Overview The pipeline automates the deployment of a Flask-based ML application, emphasizing Continuous Integration and …
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 …
This project combines Retrieval-Augmented Generation (RAG) with AWS for scalability, GitHub CI/CD for automation, and Docker for deployment reliability. It’s a practical dive into AI and DevOps that’s transforming education. Check out the full story on Medium: Read the article from developers’ perspectives, Read the article from educators’ perspectives. Github: https://github.com/shj37/medical_aws_CICD