Your data is secure with self-hosted deployment
Amazon Web Services
Kubernetes
Google Cloud
Azure
Monitoring That’s
Almost as Smart as You
Create customized monitors for your ML models in production with our magically-simple model monitor builder, and get alerts for issues like concept drift, model performance degradation and more.
Get Started in
Under 5 Minutes
Aporia integrates seamlessly with any ML infrastructure. Whether it’s a FastAPI server on top of Kubernetes, an open-source deployment tool like MLFlow or an ML platform like AWS Sagemaker.
Python Package
Easily integrate Aporia’s python package to your serving application and report live inferences.
Rest API
Using a different programming language? Use Aporia’s REST API to log inferences and get live monitoring.
Cloud Storage
Already have inference data in cloud storage? Connect Aporia to CSV and Parquet files from your cloud storage of choice.
Python Package
Easily integrate aporia’s python package to your serving application and report live inferences.
Rest API
Using a different programming language? Use Aporia’s REST API to log inferences and get live monitoring.
Cloud Storage
Already have inference data in cloud storage? Connect Aporia to CSV and Parquet files from your cloud storage of choice.
Clear Visibility to Your ML Models
See a live view of all your models in production, in one place. Monitor model activity, inference trends, data behavior, actual model performance (F1, Precision, RMSE, etc.) and more.
Clear Visibility to Production ML
See a live view of all your models in one place. Keep an eye on model activity, inference trends, data behavior, actual model performance (F1, Precision, RMSE, etc.) and more
Detect Drifts, Bias & Data Integrity Issues
Want to ensure your model is not making biased predictions against Gotham City residents between the age of 25 to 40?
Zoom into specific data segments to track model behavior. Identify unexpected bias, underperformance, drifting features and integrity issues.
Aporia also offers important data segments automatically – not necessarily where Batman lives 🦇
Investigate
the Root Cause
When there are issues with your models in production, you want to have the right tools to get to the root cause as quickly as possible.
Go beyond model monitoring with our investigation toolbox to take a deep dive into model performance, data segments, data stats or distribution.
Naturally Fits in
Your Workflow
Using MLFlow or Weights & Biases for experiment tracking? Want to get alerts on Slack or Microsoft Teams? Already have an existing infra monitoring solution like Prometheus & Grafana? No problem.
MLFlow
Slack
Microsoft Teams
JIRA
New Relic
Grafana
Prometheus
SageMaker
Loved By
See why data scientists, ML engineers, and R&D love using Aporia’s ML monitoring platform.
“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.”
“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 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 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.”
“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.”
