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 production-grade end-to-end ML system for predicting customer churn in retail banking. It uses an XGBoost model optimized for top-K targeting, with full lifecycle support including data validation, experiment tracking (MLflow), automated monitoring, drift-based retraining, and explainability via SHAP.

Analyze sales data with Python for cleaning and trends, and visualize key insights through an interactive Power BI dashboard.

An end-to-end MLOps project using Python, GitHub Actions, and Evidently AI to automate model monitoring, drift detection, and retraining.

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

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.

Implémentation complète d’un modèle de scoring crédit avec MLflow, optimisation du seuil de probabilité, interprétation SHAP et déploiement d’une API prédictive sur Heroku.

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!

End-to-end demand forecasting and inventory optimization with Databricks - multi-modal ML (time series, images, tabular), LightGBM, MAPIE conformal prediction and MLOps for retail and fashion.

Industrialisation du modèle de scoring crédit, intégrée dans le projet global de plateforme de scoring MLOps.

uses the Sage maker Unified Studio to create the end-end ML workflow for the truck-schedule classification

End-to-end ML pipeline for bike sharing demand prediction with MLflow, Prefect, Docker, FastAPI, Terraform, and Evidently AI. Includes automated training, deployment, monitoring, and CI/CD on AWS.

La plateforme de scoring automatisé avec MLOps vise à fournir un système intelligent, scalable et traçable pour : Évaluer le risque de crédit des prospects, Détecter anomalies et fraudes dans les transactions, Automatiser les rapports de conformité (KYC, AML).

End-to-end MLOps system built with MLflow, DVC, FastAPI, Feast, Docker, GitHub Actions, AWS ECS, Evidently AI, and Apache Airflow

Semantic drift detection for NLP using Sentence Transformers. Detect ML model degradation in production with embedding analysis.

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.

End-to-end fraud detection system with MLflow, FastAPI, drift detection, and automated retraining

End-to-end fraud detection ML pipeline with drift detection, SHAP explainability, and CI/CD

Hybrid forecasting (Prophet + LightGBM + Ensemble) with A/B testing in production, Evidently AI drift monitoring and automatic retraining via Airflow. MAPE <8%

ML monitoring service with data drift, model performance metrics, alerts, dashboard, PostgreSQL, Docker, and CI/CD.