ML Model Monitoring: Practical guide to boosting model performance
What is ML Model Monitoring? Machine learning model monitoring measures of how well your machine learning model performs a task...
Validate model performance
ML Monitoring
Real-time model monitoring: Detect all types of drifts and monitor billions of predictions with zero sampling.
Models break in prod, we know that most of the time it's not your fault
In production environments, unpredictability is a given. Models that were once reliable in staging can deviate due to external influences like concept drift or changes to data pipelines. Consistent model performance is vital, and with a monitoring solution, you can quickly detect and fix drifts, ensuring prediction reliability and model integrity.
Monitoring models has never been easier
Simple setup with segment-level monitoring ready in minutes
- Monitor drift, performance, and data quality across diverse model types, from LLMs to traditional ML.
- Gain peace of mind with automated monitoring of your models, data schema, and features.
- Focus on building models, let Aporia’s model monitoring do the leg work for you.
- Handling billions of predictions? Rest assured that every single prediction is monitored, leaving no room for errors or sampling.
Full customization: Your model, your rules.
Tailor ML monitoring to your unique challenges and use case.
- Monitor metrics tailored to your needs, leveraging an SQL-like query for deriving new metrics.
- Benefit from continuous auto-thresholding, making the monitoring system highly practical and efficient.
- Set static thresholds or dynamic ones that are influenced by patterns in the incoming data.
- Ensure your monitoring approach remains dynamic, adapting to your model’s evolving needs.
Examine different environments & versions
Dive deep into the nuances of your models and compare behaviours across datasets & versions
- Analyze and compare performance shifts across training, validation, and production datasets.
- Leverage native A/B testing tools to easily identify and confirm any variations in the model’s performance.
- Monitor your model’s progression and behavior across various versions and updates.
Alerts, on your terms
Get instant notifications to your preferred communication channels.
- Choose from a range of alerting options, from emails and MS Teams to Slack notifications.
- Define urgency thresholds to avoid alert fatigue.
- When drift is detected, an investigation case is instantly initiated. The goal is to solve issues, not just point to them.
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