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 # AutoML
 
-Data Science & Risk Management project using various baseball data sources to predict game outcomes and make informed sports bets. Predictions are generated using statistical models in a two-tiered approach - one at the game level, and another using a Discrete Event Simulator. The predictions are then compared against market odds to identify games with the highest potential ROI from placing bets. This is assessed across all games in a given time period, and bets are placed strategically in order to minimize downside risk while still realizing significant profits.
-- **Skills**: Statistical Modeling, Prediction, Risk Management, Problem Solving, Event Simulation
-- **Technology**: Python, XG Boost (Gradient Boosted Decision Tree) Model, Neural Network
+Automated machine learning web application, for no-code autoML functionality.
+
+***Notes**:
+  - This repo only contains a subset of the entire code in this project
+  - This project is a work in progress
+
+- **Skills**: Machine Learning, Risk Management, Problem Solving, Project Management, Full Stack Web Dev
+- **Technology**: NextJS (frontend), FastAPI (backend), Postgres + Redis + MongoDB + MinIO (databases), Kubeflow + ML Flow (ML infra), Docker + Kubernetes + Terraform (infra), Elasticsearch + Grafana + Prometheus + Filebeat (monitoring)