Automated Machine Learningml made easy
Automated ML platform
automated ml platform
Client
AutoML
Industry
AI
Headquarters
Romania
Services
UI/UX Design
About project
AutoML is an automated machine learning platform designed to help data teams build, train, and deploy models without deep ML expertise — reducing time-to-insight from weeks to hours.
Human-Readable Translation
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Data teams without specialized ML expertise were blocked from building production-grade models due to complex tooling, lengthy training cycles, and poor experiment tracking.
Zylo built an abstraction layer over ML infrastructure that guides users through dataset preparation, model selection, training, and one-click deployment with full auditability.
Process
Discovery
ML workflow audit
Data pipeline assessment
User persona mapping
Tooling evaluation
Performance baseline setting
Platform Architecture
Job orchestration design
Experiment tracking system
Model registry structure
API layer planning
Monitoring architecture
Implementation
Pipeline automation
Integration testing
Performance optimization
Deployment rollout
System Architecture
A modular ML orchestration backend handles job queuing, experiment tracking, and model versioning — all exposed through a clean REST API layer.
Interface Engineering
The dashboard surfaces training progress, model comparison metrics, and deployment status in a structured, distraction-free interface focused on decision speed.
Democratizing machine learning through structured automation and clear workflows.
MOBILE-FIRST
EXECUTION
Training status monitoring, alert notifications, and lightweight model performance reports were optimized for mobile access.
UI Kit
Component library includes training progress bars, metric comparison charts, pipeline stage indicators, and deployment status badges.
AUTOMATION
FRAMEWORK
Model training queues, hyperparameter search, and deployment pipelines are fully automated, enabling hands-free experimentation at scale.
Design system
Results
Measurable outcomes from real-world AI and engineering deployments.
12×
Faster model training cycles
+78%
Increase in experiment throughput
89%
Reduction in ML infrastructure overhead
-64%
Decrease in time-to-deployment
Verified across 500+ enterprise deployments
Let's work together
3-day AI Engineering Collaboration
Sprint
AI-driven collaboration sprint with senior engineers to design, build, and refine real-world software solutions. Focused on execution, technical depth, AI capability, and product thinking—not just ideas, but working systems.