Customized
ML Monitoring
For Your Models

Create ML monitoring tailored to your specific models and use cases in minutes with Aporia’s cloud-native ML observability platform.
customized monitors for machine learning 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

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.
custom view features in dashboard for machine learning models
custom view features in dashboard for machine learning models

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.

custom performance metrics in machine learning
custom performance metrics in machine learning

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.”

Research and development
Aviram Cohen
VP R&D at Armis
Aviram Cohen
VP R&D at Armis
“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.”

LinkedIn
Orr Shilon
ML Engineering Team Lead
Orr Shilon
ML Engineering Team Lead
“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.”

Guy Fighel
General Manager AIOps
Guy Fighel
General Manager AIOps
“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.”

Research and development
Aviram Cohen
VP R&D at Armis
Aviram Cohen
VP R&D at Armis
“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.”

LinkedIn
Orr Shilon
ML Engineering Team Lead
Orr Shilon
ML Engineering Team Lead
“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.”

Guy Fighel
General Manager AIOps
Guy Fighel
General Manager AIOps
“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.”
Start Monitoring Your Models in Minutes