evidently-ai

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.

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

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

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

Demo of Predictive maintenance for CNC machines with a full MLOps pipeline — FastAPI prediction service, automatic drift detection, and self-triggering retraining via DVC, MLflow & GitHub Actions.

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.

Professional Data Pipeline is a Python-based data quality and monitoring project that ingests DVC-backed datasets, validates tabular business rules with Great Expectations, and tracks drift in MLflow using Evidently.

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!

Enterprise-grade AI Governance Control Tower on Azure Databricks. Features active zero-trust security (Unity Catalog RLS/masking), data quality circuit breakers (Great Expectations), model observability & drift detection (Evidently AI), and immutable Delta Lake audit logging via GitOps (DABs).

End-to-end ML pipeline for bike sharing demand prediction in Seoul

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.

End-to-end MLOps system for probabilistic electricity demand forecasting across 5 Moroccan cities — LightGBM · Airflow · MLflow · FastAPI · Docker

End-to-end MLOps pipeline for Fraud Detection featuring automated CI/CD/CT, MLflow tracking, Evidently AI drift monitoring, and serverless AWS ECS Fargate deployment via CloudFormation.

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.

End-to-end MLOps pipeline for news classification — experiment tracking with MLflow, data versioning with DVC, FastAPI serving, drift monitoring with Evidently AI, and a 4-job GitHub Actions CI/CD that builds and pushes to DockerHub on every commit.

A secure, scalable FinTech platform with FraudSense (SHA-256 model signing), RegGuard (pgvector/ChromaDB compliance RAG with rate limiting), and FinLens (sanitized SQL agent). Deployed on a 3-replica HA Kubernetes cluster, utilizing Apache Kafka for asynchronous scoring to reduce fraud scoring false positives to 4.7%.

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