Advanced Data Science

Monitoring+

A rich suite of algorithms for detecting outliers, data drift, and adversarial inputs, both online and offline, across tabular, image, text, and time series data.

What You Need to Monitor ML Pipelines

Surface shifts in data distributions and model behavior early and improve model quality, maintain trust, and comply with industry or governance requirements.

Robust Monitoring

Detect drift, outliers, and adversarial inputs across tabular, image, text, and time series data using statistical and learned methods both online and offline without disrupting your ML application.

Auditable Detection

Ensure reproducible, auditable, and versioned detection pipelines with declarative configurations and consistent interfaces across all detector types and use cases.

Statistical Insights

Capture and understand statistical detection signals, including p-values, drift scores, and anomaly probabilities, across streaming and batch data for transparent, audit-ready insights integrated into Seldon’s observability stack for traceability and compliance.

Intelligent Savings

Improve model reliability and reduce the cost of failure by catching harmful distribution shifts before they impact predictions – enabling intelligent fallback, alerting, or retraining strategies.

seldon-core-2

Flexible to fit any system or requirement with built-in standardization, observability, and cost optimization, it ensures real-time predictions and efficient operations across any environment.

Spot Anomalies. Stop Failures.

Stay in Control+

Real-Time Intelligence for the Health of Your Models

Comprehensive Detection Coverage

Detect outliers, drift, and adversarial inputs across diverse data types using modular statistical, kernel, and deep learning methods, supporting both real-time and batch detection modes.

Pre-trained Models and Supported Datasets

Built-in support for pre-trained detectors and integration with standard data formats, embeddings, and model interfaces

Model-Aware and Context-Aware Detection

Incorporate model outputs, predictive uncertainty, or contextual variables into detection logic to improve signal quality and reduce false positives

Spot Anomalies. Stop Failures.

Stay in Control+

Real-Time Intelligence for the Health of Your Models

Comprehensive Detection Coverage

Detect outliers, drift, and adversarial inputs across diverse data types using modular statistical, kernel, and deep learning methods, supporting both real-time and batch detection modes.

Pre-trained Models and Supported Datasets

Built-in support for pre-trained detectors and integration with standard data formats, embeddings, and model interfaces

Model-Aware and Context-Aware Detection

Incorporate model outputs, predictive uncertainty, or contextual variables into detection logic to improve signal quality and reduce false positives

Unify, Optimize, and Scale AI with the Seldon Ecosystem

Build confidence in your model monitoring with flexible, declarative detector deployment that fits seamlessly into your production workflows.

  • Define & deploy detectors using versioned YAML configurations and production-ready pipelines for reproducible workflows, including preprocessing, thresholds, and reference data.

  • Deploy as Core 2-native components via CRDs, integrating directly with inference workflows, routers, or explainers through the Open Inference Protocol (OIP).

  • Enable asynchronous data flows using Core 2 pipelines, ensuring drift and outlier detection runs independently without impacting live performance.

Unify, Optimize, and Scale AI with the Seldon Ecosystem.

Build confidence in your model monitoring with flexible, declarative detector deployment that fits seamlessly into your production workflows.

  • Define & deploy detectors using versioned YAML configurations and production-ready pipelines for reproducible workflows, including preprocessing, thresholds, and reference data.

  • Deploy as Core 2-native components via CRDs, integrating directly with inference workflows, routers, or explainers through the Open Inference Protocol (OIP).

  • Create a replica of the model to be explained so as not to affect the throughput of production inference requests.

Get to know

Core+

Our business is your success. Stay ahead with accelerator programs, certifications, hands-on support with our in-house experts for maximum innovation. 

Accelerator Programs

Tailored recommendations to optimize, improve, and scale through bespoke, data-driven suggestions.

Hands-on Support

A dedicated Success Manager who can support your team from integration to innovation.

SLAs

Don't wait for answers with clear SLAs, customer portals, and more.

Seldon IQ

Customized enablement, workshops, and certifications.

Get to know

Core+

Our business is your success. Stay ahead with accelerator programs, certifications, hands-on support with our in-house experts for maximum innovation. 

Accelerator Programs

Tailored recommendations to optimize, improve, and scale through bespoke, data-driven suggestions.

Hands-on Support

A dedicated Success Manager who can support your team from integration to innovation.

SLAs

Don't wait for answers with clear SLAs, customer portals, and more.

Seldon IQ

Customized enablement, workshops, and certifications.

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