Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

A complete end-to-end machine learning pipeline to predict employee salaries (USD) using job-related features. Includes automated EDA, robust preprocessing, ML model training with MLflow, drift detection with Evidently AI, and a Flask-based web app for both single and batch predictions.

An end-to-end machine learning pipeline built for the US_VISA project, including data ingestion, validation, training, and prediction. Using MongoDB for storage, EvidentlyAI for drift detection, and AWS for deployment, all automated with GitHub Actions!

Repository for the smart rental and recommendation system for listings in Ghana project

monitor.ai: Non-intrusive monitoring for FDA-approved medical AI, helping healthcare organizations ensure safety and compliance without modifying validated models.

Cut through the red tape of visa processing with this production-ready, end-to-end Machine Learning solution. Built with HTML, CSS, JavaScript, Bootstrap, Python, MongoDB, and FastAPI, the solution leverages a powerful Gradient Boosting Classifier to deliver reasonably accurate predictions through a responsive, user-friendly web interface.

Kubernetes manifests to run Evidently for drift, quality, and performance monitoring. NodePort/port-forward access, optional Ingress and Prometheus/Grafana, with governance alignment to EU AI Act, NIST AI RMF, and ISO/IEC 42001.

This project helps streamline the visa approval process by using machine learning to predict the likelihood of US visa approval for employees. It can assist immigration officials in making quicker, data-driven decisions, reduce manual work, and enhance accuracy.