Model Monitoring with the WhyLabs AI Observability Platform

Frictionless Data and Model Monitoring

Enable AI Observability to detect ML issues faster, deliver continuous improvements, and avoid costly incidents

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Any data

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

Any platform

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

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.

Profile 100% of your data. No sampling. No sending data to third parties.
Integrate in minutes. Get data flowing instantly. Get alerted.
Pinpoint data drifts and data quality issues. Get alerts about training-serving skew.
Enable observability for your ML models and data right now
Track model performance continuously, in real time, at any level of granularity
Free edition, onboard in a few minutes

What people are saying about WhyLabs

“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

Getty Images

“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

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

“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

“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

Getty Images

“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

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

“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

“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

Getty Images

Easy integration

Integrate in minutes with whylogs, the open-source data logging library
Onboard the WhyLabs SaaS Platform in just three quick steps, on any ML stack
whylogs Pythonwhylogs Java

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)