Machine Learning Model Development & Deployment

About this Gig

I build machine learning models that solve real prediction problems — and I deploy them as working APIs, not just notebooks that sit on a laptop. If you need a model that predicts something (prices, risk, churn, demand, disease, sales) and you want it actually usable in your app or business, I can handle the full process: cleaning your data, building the model, testing it properly, and deploying it so it works in production. What I can build for you: - Prediction Models — regression (price, sales, demand forecasting) or classification (risk, churn, disease detection) using Scikit-Learn, XGBoost, Random Forest, and similar algorithms. - Time Series Forecasting — predicting future trends (sales, demand, weather, prices) using ARIMA, Prophet, or LSTM. - Model Deployment — I package your model as a real API (FastAPI/Flask) and deploy it on AWS, Docker, or AWS SageMaker, so it can be called from your website or app. - Monitoring Setup — for production models, I can set up monitoring dashboards (Prometheus + Grafana) so you can track model performance over time. - Data Pipelines (ETL) — cleaning and preparing large datasets so your model trains on good-quality data. Tech I work with: Python, Pandas, NumPy, Scikit-Learn, XGBoost, TensorFlow, PyTorch, FastAPI, Flask, Docker, AWS (SageMaker, S3, RDS), Prometheus, Grafana, PySpark. Proof of work — main projects: - Boston House Price Prediction API — production ML API with Docker, CI/CD pipeline, and live Prometheus/Grafana monitoring https://github.com/orbin123/boston-housing-githubactions - MNIST Digit Classifier on AWS SageMaker — full pipeline from training to a live deployed inference endpoint (~99% accuracy) https://github.com/orbin123/Machine_Learning/tree/main/Model_Deployment/WEEK2/Mini_Project - Heart Disease Prediction — XGBoost-based medical diagnosis model https://github.com/orbin123/Heart-Disease-Prediction - Telco Customer Churn — end-to-end Spark ETL + ML pipeline for churn prediction https://github.com/orbin123/Machine_Learning/tree/main/Data-Engineering/Customer-Churn-Project/spark-etl-pipeline - Climate Time Series Forecasting — comparison of ARIMA, Prophet, Exponential Smoothing, and LSTM for forecasting https://www.kaggle.com/code/orbinsunny/climate-time-series - Bulldozer Price Prediction — Random Forest regression on real-world sales data with time-series feature engineering https://github.com/orbin123/Bulldozer-Price-Prediction Full portfolio with all my projects: https://orbin-sunny-portfolio.vercel.app I focus on models you can actually use — properly evaluated, documented, and deployed — not just a Jupyter notebook with a good accuracy score.

Requirements

To get started quickly, please share: 1. Your goal — What should the model predict? (Example: "predict which customers will cancel their subscription" or "forecast next month's sales") 2. Your data — A sample of your dataset (CSV, Excel, or database export). If you're not sure your data is ready, share what you have and I'll tell you what's missing. 3. How it will be used — Should it be a one-time analysis, or a live API that your app/website calls in real time? 4. Hosting preference (if any) — If you already use AWS, GCP, or Azure. If not, I'll recommend the best option for your budget. 5. Accuracy expectations — Any specific accuracy or performance target, if you have one. 6. Timeline & budget — Your target deadline and budget range, so I can suggest the right approach. Don't worry if your data isn't clean or organized yet — that's a normal part of the process, and I can guide you through preparing it.

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