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Monitor, improve, and trust your

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

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CashApp Logo
Regions Bank Logo
Block, Inc Logo
AWS Logo
Stitch Fix Logo
Fortune 500 Retail Logo
Databricks Logo
SAP Logo
Symbl.ai Logo
Tryolabs Logo
Snappt Logo
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Fortune 500 Bank Logo
Loka Logo
Fortune 100 Healthcare Logo
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Detect, prevent, and mitigate

risk in your AI applications

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

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

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Your platform

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

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Your scale

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

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What leading AI teams are saying about WhyLabs

“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

“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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Secure Integration

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    Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security.
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    Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks.
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    Process 100% of the data in the most cost-effective integration. Never sample and never maintain sampling strategies.
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    Monitor predictive models, generative models, data pipelines, and feature stores using the same integration pattern.
  • whylogs Integration

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    ### 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()
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    Model & Data Health

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    Continuously monitor for model input and output drift. Identify training-serving skew.
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    Improve AI performance by identifying the best model candidate and the most reliable features.
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    Trace which cohorts contribute to model performance and introduce bias.
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    Proactively resolve data quality issues in feature pipelines and feature stores.
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    LLM Security

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    Production visibility and security for self-hosted and proprietary LLM APIs.
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    Enable inline actions to protect from prompts with malicious intent and abuse risk.
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    Protect your LLM application from OWASP Top 10 vulnerabilities, such as prompt injections and data leakage.
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    Continuously evaluate LLM prompts and responses to ensure consistently positive user experience.
    LLM Charts
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    Enterprise-grade

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    WhyLabs never moves or duplicates your model's raw data, instead your data is profiled using our proprietary approach.
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    Essential management features like RBAC, SAML SSO, API controls, and advanced trigger and notification configurations.
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    WhyLabs is SOC 2 Type 2 compliant and approved by security teams at healthcare companies and banks.
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    Hybrid SaaS deployment model is approved for highly confidential models and requires no maintenance.
    Enterprise Grade
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    RCA Tools

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    Segment model data into groups to pinpoint drift, bias, and data quality issues.
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    Investigate issues quickly using correlations across model input, output, and performance.
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    Trace which data segment is contributing to bias and performance.
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    Fix model issues quickly with the powerful root cause analysis tools that fuel collaboration and investigation.
    RCA Tools
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    Powerful Monitoring

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    No data sampling. WhyLabs profiles 100% of the data to deliver accurate distributions and reliable alerts.
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    Zero-config onboarding option automatically sets all crucial monitors based on model type.
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    Monitoring can be configured at scale with UI presets, JSON configurations, or via API.
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    Powerful monitoring algorithms enable intelligent baselines and seasonal monitors.
    Zero maintenance

    Seamless integration with your existing pipelines and tools

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    Run AI With Certainty

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