Managing ML models in production used to mean scattered notebooks and manual deployment processes. With this Reflex-powered platform, data science teams train, deploy, and monitor machine learning models from one unified interface. Track performance metrics, manage experiments, and ensure model reliability—all through an integrated MLOps dashboard.

Industry Technology · Finance · Healthcare · E-commerce · Research

End users Data Scientists · ML Engineers · AI Researchers · DevOps Engineers

Components Model Performance Charts · Training Progress Bars · Deployment Status Grid · Experiment Comparison Tables · Alert Notifications · Parameter Tuning Forms

• Experiment Tracking Hub – compare model runs, hyperparameter sweeps, and performance metrics with interactive visualizations and leaderboards.
• Automated Training Pipeline – schedule model retraining, handle data drift detection, and trigger alerts when performance degrades.
• Model Deployment Manager – deploy models to staging and production with A/B testing, canary releases, and rollback capabilities.
• Performance Monitoring Suite – track accuracy, latency, and resource usage with real-time dashboards and automated anomaly detection.
• Data Quality Inspector – monitor input distributions, detect drift, and validate data quality with statistical tests and visualizations.
• Model Registry Console – version control models, manage artifacts, and maintain lineage tracking across development lifecycle.

All logic is pure Python - integrate with your ML frameworks, cloud platforms, and monitoring tools to create a production-ready MLOps platform in minutes.