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Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents.

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Detect, prevent, and mitigate

risk in your AI applications

Start with reliable data. Continuously monitor any data-in-motion for data quality issues.
Pinpoint data and model drift. Identify training-serving skew and proactively retrain.
Detect model accuracy degradation by continuously monitoring key performance metrics.
Identify risky behavior in generative AI applications and prevent data leakage.
Protect your generative AI applications are safe from malicious actions.
Improve AI applications through user feedback, monitoring, and cross-team collaboration.

Your data

Structured or unstructured. Monitor raw data, feature data, predictions and actuals.

Your platform

Batch or streaming. Integrate seamlessly with existing data pipelines and multi-cloud architectures.

Your scale

Go from massive amounts of data to real-time actionable insights in minutes.

What leading AI teams are saying about WhyLabs

“We are business-to-business, and a lot of our customers don't know anything about ML. So they might make what seems to them quite as obvious and harmless changes, that has terrible impact internally. Having something like this would have prevented a lot of problems.”

Machine Learning Engineer

Sift Science

“I think tools like this could really help standardize around what types of things you're alerting on, and how you're defining those rules. And how you're visualizing it. Which would not only be useful for data scientists, but I think it would also be useful for other stakeholders. For PMs and POs, and engineers that are actively managing these products, after they are live.”

Senior Data Scientist

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“We chose WhyLabs for several reasons. First, they provide all the core model monitoring functionalities that we're looking for including a straightforward presentation of results, outlier detection, histograms, data drift monitoring, and missing feature values. [Second,] they have strong data privacy due to their aggregation of data before consumption and very fast ingestion.”

ML Platform Program Manager

Fortune 500 Fintech

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“At Airspace, we use AI to minimize risk across the supply chain for the world's most critical shipments. WhyLabs has been instrumental in driving the scalability of our AI operations. The platform offers easy onboarding, data privacy-friendly integration, and a command-center view that allows us to quickly identify and treat problems before they impact the user experience. The downstream impact of enabling observability is that we are able to continuously expand on our differentiating technology by leveraging machine learning for more use cases”

Ryan Rusnak

Co-founder and CTO, Airspace

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“We chose WhyLabs as an observability capability for our ML Platform because of the ease of integration and rich capabilities that enable us to meet Model Health Equity Governance guidelines and minimize time-to-insight across model operation tasks.”

Engineering Director

Major Healthcare Provider

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"If we're waking up engineers at 3 am, we need to be confident that we're not reporting on false positives."

IT Operations Manager

Fortune 500 Retail

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“We love how easy it was to integrate whylogs with our custom infrastructure. whylogs allows our data scientists to get insights about their datasets and monitor the models that they deploy.”

Nobuyuki Kuromatsu

Platform Engineer (MLOps), AI Platform Team
Yahoo Japan Corporation

“At Stitch Fix we have hundreds of workflows that connect to production microservices all driven and deployed by Algorithms team members. Observability is essential to ensure that these services are robust and deliver consistent customer experiences. We are excited to collaborate with WhyLabs on building an open source standard for data logging that helps us streamline observability across our data and AI pipelines, be it offline or online.”

Stefan Krawczyk

Manager of Model Lifecycle, Stitch Fix

“ML engineers need better tools to ensure high-quality data through all stages of an ML project's lifecycle. AI Fund is excited to support WhyLabs, whose open source logging library and AI observability platform makes it easy for developers to maintain real time logs and monitor ML deployments.”

Andrew Ng

Managing General Partner, AI Fund

“We need tools that enable our machine learning team to ensure AI models help inform seamless experiences for customers and achieve business objectives when running at a very high scale. WhyLabs' monitoring solution takes a practical and elegant approach to monitoring the input and output data, statistics and behavior of models in flight at scale, filling the gap between software and machine learning model operations.”

Olly Downs

VP of Martech, Data and Machine Learning, Zulily

Machine Learning Engineer

Sift Science

Senior Data Scientist

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ML Platform Program Manager

Fortune 500 Fintech

Read the case study

Ryan Rusnak

Co-founder and CTO, Airspace

Read the case study

Engineering Director

Major Healthcare Provider

Read the case study

"If we're waking up engineers at 3 am, we need to be confident that we're not reporting on false positives."

IT Operations Manager

Fortune 500 Retail

Read the case study

“We love how easy it was to integrate whylogs with our custom infrastructure. whylogs allows our data scientists to get insights about their datasets and monitor the models that they deploy.”

Nobuyuki Kuromatsu

Platform Engineer (MLOps), AI Platform Team
Yahoo Japan Corporation

Stefan Krawczyk

Manager of Model Lifecycle, Stitch Fix

Andrew Ng

Managing General Partner, AI Fund

Olly Downs

VP of Martech, Data and Machine Learning, Zulily

Machine Learning Engineer

Sift Science

Senior Data Scientist

Getty Images

Secure Integration

Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security.

Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.

Process 100% of the data in the most cost-effective integration. Never sample and never maintain sampling strategies.

Monitor predictive models, generative models, data pipelines, and feature stores using the same integration pattern.

whylogsLangKit

whylogs Integration

PYTHON

flask

sagemaker

### First, install whylogs with the whylabs extra
### pip install -q 'whylogs[whylabs]'

import pandas as pd
import os
import whylogs as why

os.environ["WHYLABS_API_KEY"] = "YOUR-API-KEY"
os.environ["WHYLABS_DEFAULT_ORG_ID"] = "YOUR-ORG-ID"
os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "model-1" # Note: the 'model-id' is provided when setting-up a model in WhyLabs

# Point to your local CSV if you have your own data
df = pd.read_csv("https://whylabs-public.s3.us-west-2.amazonaws.com/datasets/tour/current.csv")
                
# Run whylogs on current data and upload to the WhyLabs Platform
results = why.log(df)
results.writer("whylabs").write()

Model & Data Health

Continuously monitor for model input and output drift. Identify training-serving skew.

Improve AI performance by identifying the best model candidate and the most reliable features.

Trace which cohorts contribute to model performance and introduce bias.

Proactively resolve data quality issues in feature pipelines and feature stores.

LLM Security

Production visibility and security for self-hosted and proprietary LLM APIs.

Enable inline actions to protect from prompts with malicious intent and abuse risk.

Protect your LLM application from OWASP Top 10 vulnerabilities, such as prompt injections and data leakage.

Continuously evaluate LLM prompts and responses to ensure consistently positive user experience.

Enterprise-grade

WhyLabs never moves or duplicates your model's raw data, instead your data is profiled using our proprietary approach.

Essential management features like RBAC, SAML SSO, API controls, and advanced trigger and notification configurations.

WhyLabs is SOC 2 Type 2 compliant and approved by security teams at healthcare companies and banks.

Hybrid SaaS deployment model is approved for highly confidential models and requires no maintenance.

RCA Tools

Segment model data into groups to pinpoint drift, bias, and data quality issues.

Investigate issues quickly using correlations across model input, output, and performance.

Trace which data segment is contributing to bias and performance.

Fix model issues quickly with the powerful root cause analysis tools that fuel collaboration and investigation.

Powerful Monitoring

No data sampling. WhyLabs profiles 100% of the data to deliver accurate distributions and reliable alerts.

Zero-config onboarding option automatically sets all crucial monitors based on model type.

Monitoring can be configured at scale with UI presets, JSON configurations, or via API.

Powerful monitoring algorithms enable intelligent baselines and seasonal monitors.

Seamless integration with your existing pipelines and tools

Run AI With Certainty

Book a demo

Monitor, improve, and trust your

Detect, prevent, and mitigate

risk in your AI applications

Your data

Your platform

Your scale

What leading AI teams are saying about WhyLabs

Secure Integration

Model & Data Health

LLM Security

Enterprise-grade

RCA Tools

Powerful Monitoring

Seamless integration with your existing pipelines and tools

Run AI With Certainty

About

Resources

whylogs

WhyLabs