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Aquarium
Identify and fix critical ML model bottlenecks through intelligent data curation
About Aquarium
Aquarium is a machine learning data curation platform that transforms how teams approach model improvement. By leveraging advanced embedding technology, Aquarium automatically detects the most critical performance bottlenecks affecting model accuracy, eliminating guesswork from the data engineering process. The platform enables ML teams to systematically address data quality issues through targeted interventions rather than broad, inefficient retraining efforts. Aquarium's automated issue detection surfaces hidden problems in training datasets that would otherwise go unnoticed, allowing data scientists to focus optimization efforts where they matter most. Through the AiDOOS marketplace, organizations gain access to pre-configured deployment architectures, governance frameworks, and integration patterns that accelerate time-to-value. The platform scales seamlessly across enterprise ML workflows, supporting multiple models and datasets while maintaining detailed audit trails for reproducibility and compliance.
Challenges It Solves
- ML models degrade in production due to unidentified data quality issues and distribution shifts
- Data teams spend excessive time manually investigating model performance bottlenecks
- Organizations lack visibility into which data samples are truly driving model inaccuracy
- Model improvement efforts are unfocused, leading to wasted resources on low-impact data work
Use Cases
Computer Vision Model Optimization
Identify corrupted, mislabeled, or edge-case images causing vision model degradation. Aquarium pinpoints specific data samples requiring correction or removal for significant accuracy improvements.
NLP Model Performance Enhancement
Detect linguistic edge cases, labeling inconsistencies, and domain shift issues in text datasets. Focus annotation efforts on the most impactful training examples for language models.
Production Model Monitoring & Drift Detection
Monitor deployed models for data distribution shifts and emerging failure patterns. Trigger proactive retraining only when Aquarium detects significant performance risk.
Dataset Quality Assessment for Model Training
Evaluate training dataset quality before expensive model training runs. Identify and remediate fundamental data issues to ensure training investment yields strong baseline performance.
ML Operations and Governance
Maintain centralized curation audit trails and governance policies across enterprise ML teams. Ensure consistent data quality standards and regulatory compliance across all models.
Pricing
Pricing available on request
Aquarium pricing is customized based on your team size, integrations, and requirements. AiDOOS will get you a scoped proposal — for free.
Key Features
Instantly surface critical model bottlenecks
Identifies performance-impacting data issues in minutesAdvanced semantic understanding of data relationships
Pinpoints subtle data patterns affecting model accuracyFocused improvement recommendations
Delivers high-impact curation actions with measurable ROIManage data curation across entire ML portfolios
Monitor and optimize multiple models simultaneouslyReal-time visibility into data quality and model impact
Track improvement metrics across all curation activitiesMaintain detailed records of all data interventions
Ensures reproducibility and regulatory complianceReviews
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Enterprise Readiness
Integrations
8 total appsNative integration for ML workflows, enabling seamless embedding analysis within data science notebooks
Direct compatibility with major deep learning frameworks for model evaluation and feedback loops
Cloud-native integration for model training and deployment pipelines on AWS infrastructure
Container orchestration support for scalable, production-grade ML operations
Large-scale distributed data processing integration for enterprise datasets
Integration with data versioning tools for reproducible ML pipelines and audit trails
Experiment tracking and model registry integration for comprehensive ML lifecycle management
Data warehouse connectors for seamless dataset access and large-scale analysis
AiDOOS Managed Deployment
Deploy Aquarium in
AiDOOS handles setup, CRM integration, SSO config, and user provisioning. Your team goes live — not your IT department.
Prerequisites
Configuration Options
A Virtual Delivery Center for Aquarium
Pre-vetted experts and AI agents in the loop, assembled as a delivery pod. Pay in Delivery Units — universal pricing across roles, seniority, and tech stacks. No hiring, no contracting, no procurement cycle.
- Plans from $2,000 — Starter Pack, 10 Delivery Units, 90 days
- Refundable on unused Delivery Units, anytime — no questions asked
- Re-delivery guarantee on acceptance miss
- Pre-flight delivery sizing — you see the plan before you commit
How a Virtual Delivery Center delivers Aquarium
Outcome-based delivery via AiDOOS’s VDC model. Why VDC vs traditional consulting? →
Outcome-Based
Pay for results, not hours
Milestone-Driven
Clear deliverables at each phase
Expert Network
Access to certified specialists