Customized
ML Monitoring
For Your Models
New Value
Monitor selected features and raw inputs for new values
Prediction Drift
Monitor selected predictions for
a distribution drift
Data Drift
Monitor selected features and raw inputs for a distribution drift
Perfomance Degradation
Monitor degradation in model’s predictions and features
Model Activity
Monitor the amount of predictions the model has performed
Model Staleness
Monitor that a model’s versions are being updated regularly
Code-based Monitor
Monitor anything by fully customizing your own monitor with python code
Build Your Own Custom Monitoring
Use Aporia’s magically-simple monitor builder to create over 50 different customizable monitors for data drift, bias, data integrity issues, performance degradation, and more in minutes.
Choose from automated monitors or code-based monitors to create ML monitoring that fits your specific use case and models.
Build Your Own Custom Monitoring
Data Drift
Monitor selected features and raw inputs for a distribution drift
Prediction Drift
Monitor selected predictions for a distribution drift
Perfomance Degradation
Monitor degradation in model’s predictions and features
Model Activity
Monitor the amount of predictions the model has performed
Code-based Monitor
Monitor anything by fully customizing your own monitor with python code
New Value
Monitor selected features and raw inputs for new values
Model Staleness
Monitor that a model’s versions are being updated regularly
Custom Views & Dashboards
Get a single pane of glass with everything you need to know about your models in production with Aporia. Instantly build your own custom dashboards for your specific model use case. From fraud detection to demand forecasting, and credit risk, gain the most relevant insights about your ML models at any time.
With Aporia, you can analyze how your models arrive at their predictions, understand how a change in a feature impacts a prediction, and prevent
issues like bias and drift in the future.
Use our Data Point Explainer to debug your data at a specific point, and then
re-explain in one click.
Custom Metrics & Code-Based ML Monitoring
Get to the Root Cause
Custom Metrics & Code-Based ML Monitoring
Want to implement your own custom monitoring logic? Or define your own monitoring metrics?
Select Absolute Values, Anomaly Detection, or Change in Percentage to begin creating your own custom metrics – or take customized monitoring to the limit with Aporia’s code-based Python monitors.
Find the root cause of any issue and discover when it started using our Data Points and Time Series Investigation Tools.
Aporia makes it easy to slice and dice your data to quickly drill down into your model’s data points and the impact on prediction results.
Loved By
See why data scientists, ML engineers, and R&D love using Aporia.
“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”
“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models’ performance and take full control of it.”
“With Aporia’s customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”
“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”
“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models’ performance and take full control of it.”
“With Aporia’s customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”