Sergio W.

I specialize in taking trained models and turning them into deployable services with real-time inference, clean interfaces, and reproducible pipelines. My focus is on making ML systems accessible, scalable, and production-oriented rather than purely experimental.

Core focus areas:

Deployment of machine learning models using Hugging Face Spaces and Gradio

Backend integration for ML inference workflows

Data preprocessing, feature engineering, and model training in Python

Building end-to-end pipelines from dataset to deployed application

API-style model serving and interactive web interfaces

Selected project:

ATP Prediction Engine – Alpha Decay Market Regime Analysis

A structured ML system for probabilistic prediction and regime analysis, combining statistical modeling and feature-based forecasting.

GitHub:

Deployment experience:

Deployed machine learning models on Hugging Face with Gradio interfaces, enabling interactive inference and demonstrating practical backend deployment of ML systems.

Technical stack:

Python, pandas, NumPy, scikit-learn (applied), Hugging Face, Gradio, Jupyter, data visualization tools