Machine Learning Hubintelligent insights
Machine learning dashboard
machine learning dashboard
Client
SmartAI
Industry
AI
Headquarters
Israel
Services
UI/UX Design
About project
SmartAI is a machine learning dashboard platform that gives data teams unified visibility into model performance, experiment tracking, and production monitoring across ML workloads.
Challenges & Solutions
Problem
Data science teams lacked unified visibility into model performance across environments — making it difficult to detect degradation, compare experiments, or prioritize retraining decisions.
Solution
Zylo designed a structured ML observability dashboard that centralizes experiment tracking, production monitoring, and model health scoring in a single, navigable interface.
Process
Discovery
ML observability gap analysis
Experiment tracking review
Model health metric design
Data drift assessment
Team workflow research
Platform Architecture
Metrics aggregation layer
Experiment tracking system
Drift detection logic
Alert engine design
Dashboard data structure
Implementation
Integration development
Model validation
Performance testing
Data team rollout
System Architecture
A metrics aggregation backend collects model performance signals, data drift indicators, and experiment metadata — feeding real-time dashboards and automated alert systems.
Interface Engineering
Model performance views, experiment comparison tables, and drift detection charts are organized in a structured, filterable dashboard designed for fast pattern recognition.
Complete ML visibility — from experiment to production.
MOBILE-FIRST
EXECUTION
Mobile model monitoring provides key performance alerts, production health summaries, and retraining recommendation notifications.
UI Kit
Component library covers model score cards, drift detection charts, experiment comparison views, alert panels, and training progress indicators.
AUTOMATION
FRAMEWORK
Data drift detection, model performance scoring, retraining triggers, and experiment result archiving are fully automated.
Design system
Results
-68%
Reduction in model degradation detection time
+74%
Improvement in experiment throughput
4×
Faster retraining decision cycles
89%
Production model uptime maintained above SLA
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