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Image Alt By deploying Aporia’s architecture in their AWS environment, customers can monitor all of their models in production in minutes, whether on SageMaker, Kubernetes or another platform.

ML Observability with Aporia on AWS

Aporia’s ML Observability platform is fully integrated into the AWS ecosystem, including Sagemaker, Redshift, S3, Athena, and Glue.

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Aporia’s partnership with Amazon Web Services (AWS) provides easy and reliable ML observability to AWS customers. By deploying Aporia’s architecture in their AWS environment, customers can monitor all of their models in production in minutes, whether on SageMaker, Kubernetes or another platform.

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Deploy Aporia to your
AWS account

02

Integrate your models by
connecting to any data source

03

Start using
Aporia!

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Connect to
Any Data Source

Aporia connects directly to your models’ training and inference data in S3, Athena, Glue, and Redshift. Storing your inference data is crucial not only for model monitoring, but also for model retraining, auditing, and incident investigation purposes.

You can then easily visualize your models with customizable dashboards for ML-specific metrics (e.g AUC ROC widget), advanced alerts on data drift and model performance degradation, and analysis of different data segments.

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Integrates natively with
Sagemaker

Aporia has native integration with SageMaker. When this capability is enabled, SageMaker will automatically store the model's features and predictions in an S3 bucket, which Aporia natively connects to.

With the Aporia - SageMaker integration, ML teams can streamline and scale their ML processes with an end-to-end solution for tracking and managing the entire ML lifecycle - from training to production, and beyond.

Aporia has a native integration with SageMaker for any model with request-response logging enabled. When this capability is enabled, SageMaker will automatically store the features and the predictions of the model in an S3 bucket, which Aporia natively connects to. Aporia has a native integration with SageMaker for any model with request-response logging enabled. When this capability is enabled, SageMaker will automatically store the features and the predictions of the model in an S3 bucket, which Aporia natively connects to.
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Start Monitoring Your Models in Minutes

Try IT Out Get a Demo
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More than just a Monitoring platform

Monitor, Visualize, & Improve Your Models in Production

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Visibility

Centralized, real-time view of model health & performance

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Live Alerts

Detect drifts, bias and data integrity issues

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Investigation

Get to the root cause and improve models with production data

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Explainability

Know the why behind your models predictions

Why Aporia on AWS?

ML Dashboards

Visualize & Share models performance

Get a unified view of all your models under a single hub. Keep an eye on model activity, inference trends, data behavior, model performance (f1 score, Precision, RMSE, etc).

Learn more

Live Alerts

Detect drift, bias & data integrity issues

Get live alerts to Slack / MS Teams on any drift, bias, performance, or data integrity issues.

Learn more

Explainability

Explain model predictions

Discover which features impact your predictions the most, and easily communicate model results to key stakeholders

Learn more
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Integrations

Aporia naturally fits into your workflow

DevOps
Amazon S3 Amazon S3
Glue Glue
Snowflake Snowflake
BigQuery BigQuery
Databricks Databricks
Azure Blob Storage Azure Blob Storage
Redshift Redshift
PostgreSQL PostgreSQL
PostgreSQL PostgreSQL
Delta Lake Delta Lake
Spark Spark
Ray Ray
New Relic New Relic
Grafana Grafana
Prometheus Prometheus
DataDog DataDog
Azure ML Azure ML
Vertex AI Vertex AI
AWS Sagemaker AWS Sagemaker
TensorFlow TensorFlow
LightGBM LightGBM
Hugging Face Hugging Face
Scikit Learn Scikit Learn
PyTorch PyTorch
dmlc XGBoost dmlc XGBoost
CatBoost CatBoost
Spark MLib Spark MLib
Slack Slack
Microsoft Teams Microsoft Teams
Jira Jira
PagerDuty PagerDuty
Opsgenie Opsgenie
Email Email
Webhook Webhook
MLFlow MLFlow
Kubeflow Kubeflow
KServe KServe
DVC DVC
Feast Feast
Weights & Biases Weights & Biases
ClearML ClearML

Data Sources

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Amazon S3

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Glue

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Snowflake

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BigQuery

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Databricks

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Azure Blob Storage

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Redshift

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PostgreSQL

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PostgreSQL

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Delta Lake

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Spark

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Ray

DevOps Infrastructure Monitoring

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New Relic

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Grafana

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Prometheus

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DataDog

Cloud Providers

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Azure ML

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Vertex AI

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AWS Sagemaker

ML Frameworks

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TensorFlow

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LightGBM

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Hugging Face

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Scikit Learn

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PyTorch

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dmlc XGBoost

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CatBoost

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Spark MLib

Alerting

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Slack

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Microsoft Teams

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Jira

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PagerDuty

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Opsgenie

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Email

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Webhook

MLOps - Complementary MLOps Tools

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MLFlow

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Kubeflow

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KServe

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DVC

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Feast

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Weights & Biases

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ClearML

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Start using Aporia now

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