Everything you need for AI Performance in one platform.
We decided that Docs should have prime location.
Fundamentals of ML observability
Metrics, feature importance and more
We’re excited ???? to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Create ML monitoring tailored to your specific needs, models, and use cases in minutes with Aporia’s ML observability platform.
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.
Monitor selected features and raw inputs for a distribution drift
Monitor selected features and raw inputs for new values
Monitor selected predictions for a distribution drift
Monitor degradation in model’s predictions and features
Monitor that a model’s versions are being updated regularly
Monitor anything by fully customizing your own monitor with python code
Monitor the amount of predictions the model has performed
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.
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.
“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”
Principal, MLOps & Data Engineering
“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."
ML Engineering Team Lead
“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.”
VP R&D
“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.”
General Manager AIOps
“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”
Co-Founder | VP R&D
