Data Scientist, Engineer, AI, 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 is a hands-on implementation of a real-time algorithmic trading system that processes market data and news sentiment to make automated trading decisions. It integrates Apache Flink for stream processing, Redpanda for data streaming, and the Alpaca API for market data and trade execution, with sentiment analysis driving the strategy. Technologies Used How It …
This project is a real-time anomaly detection system for stock trading data, built with Redpanda and Quix. It processes streaming trade data and flags unusual patterns using both rule-based and machine learning techniques. Purpose The goal is to detect anomalies—like sudden price jumps or high-volume trades—as they happen, using a lightweight, practical setup. Technologies Used …
In today’s data-driven world, real-time monitoring of database changes is critical for applications like fraud detection and live analytics dashboards. This project demonstrates a robust real-time data pipeline using PostgreSQL, Debezium, and Kafka, orchestrated with Docker, to capture and stream database changes efficiently. The project includes: Features Technology Used Learn More Link: Read the full …
This repo shows how to use AWS EMR and Apache Spark to process big data in the cloud. It includes scripts and notes from my Medium guide on setting up and running Spark jobs. Key Features Tech Stack Link: Read the full article on Medium Code: https://github.com/shj37/AWS-EMR-Scalable-Spark-Processing
Summary Skills: Fine Tuning · Large Language Models (LLM) · Natural Language Processing (NLP) · TPU · GPU · Artificial Intelligence (AI) · LoRA · QLoRA · FinTech · financial risk compliance · Chatbot Development · Keras · PyTorch · Project Management · Gemma-2b · Gemma-7b · Data Cleaning · Synthetic Data Generation Highlights Project …
Role: Student AI Researcher at Skinopathy Skills: Artificial Intelligence (AI) · speech-to-text · Fine Tuning · QLoRA · Natural Language Processing (NLP) · Text-to-Speech Synthesis · Data Processing · Data Cleaning · OpenAI Whisper · Audio Recognition · Project Management
I conducted some preliminary tests to assess the efficacy of employing GPT-4 for analyzing reference documents and generating answers for tasks assigned in the first semester of my graduate certificate program. It’s worth noting that this project was carried out post-completion of those courses, and therefore, was not utilized for the assignments associated with those …