Proactive insights, intelligent alerts, and scalable monitoring for peak model performance.
The insights engine behind trustworthy AI
KEY FEATURES
Mona | A Model
Performance
Insights Platform™
Mona’s monitoring solution detects anomalies in granular segments, giving you precise, actionable insights to quickly diagnose issues and optimize model performance.
Customizable Monitoring
Tailor your monitoring schema with bespoke metrics and dimensions, setting up comprehensive monitors for any scenario.
Automatic Performance Insights
Dive deep into your data with Mona’s ability to slice through multiple dimensions, isolating anomalies without noise.
Scalable & Secure Solution
Engineered for the most demanding datasets, Mona offers both on-premises (+ air-gapped) and cloud deployments.
Real-time & Batch Processing
Stay ahead with instant anomaly detection and comprehensive historical analyses, ensuring no opportunity for optimization is missed.
MONA DASHBOARD
From Detection to Diagnosis—All in One View
View summary insights, including the metric showing an anomaly and the specific segments where it occurs.
Analyze the impact, like the significance of the anomaly and how much of the data is affected.
Identify related symptoms that may be linked to the anomaly, offering further context for investigation.
Visualize the anomalous behavior to better understand when and how it developed.
Review correlated metrics with anomalies in the same data segment.
QUANT MONITORING USE CASES
Specialized Monitoring for
Complex Industries
Designed to handle models with the highest levels of risk, scale,
and complexity, Mona empowers teams to maintain control and precision.
Explanations for PnL Leaks
Gain deeper insights into PnL leaks with automated detections and root cause analysis for faster resolutions and system optimizations.
Time-Specific Model Deviations
Pinpoint exact times when new model versions diverge, ensuring consistency and reliability.
Inference & Training Data Discrepancies
Ensure model stability by monitoring and aligning inference-time and training-time feature sets.
Feature Correlation Drifts
Detect and address shifts in feature and output correlations over time, maintaining your model's predictive power.
Environment Behavior Comparisons
Identify discrepancies between testing, staging, and production environments, enabling smooth transitions and deployments.
Region-Specific Anomalies
Uncover and rectify unusual model behaviors across different exchanges or regions, safeguarding your assets.
MONA BENEFITS
Risk Reduction
Protect your investments from unforeseen model discrepancies and errors.
Enhanced Efficiency
Streamline your research process, freeing up valuable time for innovation and strategy refinement.
Quick Resolution
Rapidly identify and rectify issues, reducing downtime and negative financial impact.
Continuous Improvement
Leverage detailed insights to fine-tune models, enhancing performance and profitability over time.
Avoid Monitoring’s Number One Enemy: Noise
Alert fatigue is the leading cause of monitoring failures—but noise can be eliminated. Mona’s Common Thread Analysis™ reduces alert volume by an average of 80% per month, drastically reducing time to resolution.
Model Operations Time Savings Calculator
This calculator is for informational purposes only and offers general estimates of potential time savings in MLOps processes. Results are hypothetical and not a substitute for professional advice. No guarantees are made regarding accuracy or completeness. Consult a qualified professional for specific guidance.
Mona could save you as much as
hours / month
Monitor Your Entire AI Program
Gain full visibility and control across all your models –
from AI and ML to predictive analytics, financial models, and beyond.
How Granularity in Model Monitoring Saves Quants from Costly Mistakes
Itai Bar Sinai, Co-founder and CPO
Model monitoring at quant funds can feel like a constant fire drill—issues go unnoticed until it’s too late, and small missed tweaks could have made a big impact. The culprit? A lack of granularity. Here’s how deeper monitoring can change that.
The Need for Specialized Monitoring in Quantitative Trading Models
Yotam Oren, Co-founder and CEO
Quant models power automated trading at unmatched speeds, but traditional monitoring—focused on broad metrics and infrastructure—often misses critical performance issues. To optimize model performance, monitoring must do more.
How to Ensure Consistent Performance in Quant Trading Systems
Yotam Oren, Co-founder and CEO
Automated quantitative trading systems are sophisticated, but they face a myriad of challenges that can disrupt their performance. From issues with data and models to internal operations, there’s a lot that can go wrong. Let's discuss.
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