The most advanced ML Observability platform
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
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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.
See why data scientists, ML engineers, and R&D love using Aporia.
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.”
“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.”
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.”
Co-Founder | VP R&D
“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.”
“We develop and deploy models that impact students’ lives across the country, so it’s crucial that we have good insight into model quality while ensuring data privacy. Aporia made it easy for us to monitor our models in production and conduct root cause analysis when we detect anomalous data.”
“As an early stage startup, starting to launch ML models in the fintech sector, monitoring the predictions and changes in our data is critical, and Aporia has made it easy by providing the right integrations and is easy to use.”