Explainable Monitoring = High-Performance

AI has unique operational challenges that need continuous monitoring: from identifying drifting data and performance dips to pinpointing outliers, Fiddler keeps your AI on track with Machine Learning performance monitoring.

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An image of Fiddler's Machine Learning Model Performance Management Platform's Monitoring features. Our product helps users to maintain high model performance and reduce troubleshooting time by using Explainable AI. With XAI, users can debug ML issues such as data drift, data integrity, and outliers faster.
An illustration of MLOps without Fiddler's ML MPM Platform. Users such as Data Scientists and ML Engineers do not have real-time visibility and cannot resolve model issues like data drift, data integrity, outliers, performance, and bias quickly.
An illustration of MLOps with Fiddler's ML MPM Platform. Thanks to Explainable AI, Fiddler's ML Model Performance Management Platform helps data scientists and ML engineers to quickly find and solve ML Issues such as data drift, data integrity, outliers, performance, and bias.

Unique operational challenges in ML models

Lack of in-depth visibility into deployed systems results in suboptimal solutions and potentially harmful outcomes

Unique characteristics of ML models demand robust ML performance monitoring to manage issues like model decay

Inherent opaqueness of machine learning models makes them harder to understand

Fiddler helps you answer questions such as, "Is your data drifting?", "Is your AI fair?", and "Do you have outliers?"

Fast Issue Resolution

An animation of Fiddler's Machine Learning Model Performance Management Platform. It shows how a user can drill down into an outlier.

Mitigate challenges with solving production ML issues by unlocking blackbox AI models using explainability and ML monitoring tools

Deep dive into production issues using explainability and model analytics with global, local, and regional comparisons

Save time debugging issues with deeper insights and reasoning behind model behavior

Streamlined detection of data changes with ML model monitoring

Use data drift detection to maintain quality without direct indicators of model performance

Dive into the causes behind data drift for faster resolution

Use the specifics behind problem drivers to better inform model retraining

A screenshot of Fiddler's product. This screen shows how a user can investigate data drift and how explainability assists in finding the root cause.

Seamless Pluggability

Fiddler's out-of-the-box machine learning monitoring integrations are easily pluggable with existing data and Al infrastructure making it flexible to use. Try our Machine Learning model monitoring tools today.

Plug into any model framework

python logoaws sagemaker logoh2o.ai logo
scikit learn logotensorflow logopytorch logosas

Ingest from any data source

aws amazon redshift logosinglestore logoamazon aws s3 logosalesforce logo
google bigquery logosnowflake logopostgresql logohadoop logo

Stay on top of anomalies

A GIF animation of Fiddler's Monitoring features. Fiddler allows users to get alerts on drift, integrity, and outlier issues.

Monitoring ML models allows you to detect outliers easily and understand which ones are critical, threat or otherwise.

Get a bird’s eye view of all your outliers or easily pinpoint those caused by a specific model input

Probe into each outlier using one-click explanations for fast problem assessment

Take immediate action with alerts.

Set it and forget it. Get alerted for a wide range of issues and manage your model’s unique needs.

Solve issues quickly by zooming into alerts, troubleshooting in context,  and identifying the root cause

Use a powerful alert management dashboard to effectively manage all your alerts

An image of Fiddler's ML MPM Platform dashboard. It shows how a user can configure alerts when data drift occurs.

Build trustworthy and explainable AI solutions with Fiddler.

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