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Run AI with Certainty

Enable AI Observability to achieve healthy models, fewer incidents, and happy customers.

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Any data

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

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Any platform

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

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Any scale

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

AI Observability for everyone.

Free forever, no credit card needed.

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

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sagemaker

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"

df = pd.read_csv("YOUR-DATASET.csv")

# 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' 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 peformance 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 degradations
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Configure and monitor any custom model metric or KPI
Model health / Mdoelops
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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/perimiter. All WhyLabs product features operate on statistica 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 maintanence. 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

Seamless Integration
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What people are saying about WhyLabs

“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.”

VP of Martech, Data and Machine Learning, Zulily

“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

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Run AI With Certainty

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