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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.

No credit card needed. Sign up for the free Starter plan today.

Trusted by:

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Observability for everyone.

Free forever, no credit card needed.

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    Profile 100% of your data. No sampling.No sending data to third parties.
  • checkmarkPinpoint data drifts and data quality issues. Get alerts about training-serving skew.
  • checkmarkTrack model performance continuously, in real time, at any level of granularity.
  • checkmarkIntegrate in minutes. Get data flowing instantly. Get alerted in real-time.
  • checkmarkEnable observability for your ML models and data right now.
  • checkmarkFree edition, onboard in a few minutes
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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 people 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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Easy integration

  • checkmarkIntegrate in minutes with whylogs, the open-source data logging library
  • checkmarkOnboard the WhyLabs SaaS Platform in just three quick steps, on any ML stack

Language:

Python

Java

Integration:

Basic

flask

sagemaker

### WhyLabs Platform support for whylogs v1 is coming soon!
### The following WhyLabs Platform integration example requires the latest whylogs v0 version: 
### pip install "whylogs<1.0"

import pandas as pd
import os
from whylogs.app import Session
from whylogs.app.writers import WhyLabsWriter

os.environ["WHYLABS_API_KEY"] = "YOUR-API-KEY"
os.environ["WHYLABS_DEFAULT_ORG_ID"] = "YOUR-ORG-ID"

# Adding the WhyLabs Writer to utilize WhyLabs platform
writer = WhyLabsWriter()

session = Session(project="demo-project", pipeline="demo-pipeline", writers=[writer])

# 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
# Note: 'datasetId' maps to 'model-id' that is provided when setting-up a model in WhyLabs
with session.logger(tags={"datasetId": "model-1"}) as ylog:
    ylog.log_dataframe(df)
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Data health / DataOps

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Catch missing data, null values, schema changes, and other data quality issues automatically
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Prevent training-serving skew by continuously monitoring against a training data baseline
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Pinpoint data drifts and data bias before they impact the user experience
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Monitor the Feature Store to detect outages and drifts
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Model health / ModelOps

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Continuously track model outputs and model performance for any model type
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Debug model behavior anomalies quickly, with smart correlation and visualization
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Get alerted about concept drift and model accuracy degradation
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Configure and monitor any custom model metric or KPI
Model health / ModelOps
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Privacy preserving

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WhyLabs profiles model inputs and outputs to capture only statistical profiles of the underlining data
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The raw data never leaves the customer VPC/perimeter. All WhyLabs product features operate on statistical profiles
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Statistical profiles do not contain proprietary information or PII
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All statistical profiles are encrypted during transfer and at rest
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Zero maintenance

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No schema maintenance. The integration layer automatically infers data schema.
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No monitoring configuration. Simply pick your baseline and sensitivity.
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No data sampling. WhyLabs profiles 100% of the data to deliver accurate distributions.
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No deployment pain. WhyLabs is a SaaS AI Observability layer that suits even the most secure organization.
Zero maintenance

Seamless integration with your existing pipelines and tools

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