What do you do?

Aporia is the ML Observability platform, designed to ensure optimal model performance in production. It allows you to monitor for data drift, model degradation, bias, and data integrity issues at scale, with live alerts for early issue detection. Aporia centralizes all models under a single hub, enabling efficient tracking of key metrics and customization of dashboards, monitors, metrics, and segments to suit your specific use case.

The platform offers a streamlined model integration process, facilitating the direct connection to various data sources (Redshift, S3, Databricks, Snowflake, BigQuery, Athena), and monitoring of billions of predictions without data duplication. With features such as live alerting, explainability, and all-in-one root-cause analysis, Aporia empowers teams to investigate root cause, explore production data in a collaborative, notebook-like experience, and gain powerful insights to improve model performance. Use Aporia to enhance your ML’s business impact, increase efficiency, and ensure reliable and accurate ML model performance.

How much does it cost?

Aporia is free to use for up to 1 GB with Aporia’s cloud deployment.

Professional plans include on-VPC deployment and unlimited models. Pricing is based on the number of predictions and data retention.

Annual per-model contract

Learn more about Aporia’s pricing.

What’s a sample use case? Where can I learn from?

Use Cases & Links:
Aporia supports all use cases – 

And every model – 

LLM / NLP / Tabular / Computer Vision

Feature List

    Feature list:

    a. Centralized model management under a single hub to track data integrity and model behavior. 

    b. Dashboards: Allows users to create, share, and tailor their own dashboards, offering various visualization widgets to track model metrics, performance, and behavior over time.

    c. Versions Tracking: Aporia can track and manage multiple versions of the same model, providing insights on performance across versions.

    d. Advanced Model Monitoring: Real-time monitoring and alert system for issues such as data drift, missing values, bias, performance degradation, model staleness, and data integrity issues.

    e. Live Alerts: Detailed alerting system via Slack, MS Teams, and email for any issues in production.

    f. Custom Metrics: Allows creation and tracking of bespoke metrics relevant to specific use cases.

    g. Production IR: Collaborative notebook experience for fast root cause analysis (RCA) and data exploration involving multiple stakeholders.
    Tools include:

    i. Segment, drift, and distribution analysis

    ii. Embedding Projector with UMAP

    iii. Text cells for reporting and collaborating on RCA

    iv. Data stats

    h. Explainable AI: Understand the logic behind model predictions, simulate what-if scenarios, and explain model decisions to stakeholders. 

    i. Direct Data Connectors (DDC): Directly connect to your data sources, eliminating the need for data duplication or changes in production code. This allows teams to monitor billions of predictions, without jeopardizing your sensitive data.


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