blog bg left
Back to Blog

WhyLabs Now Available in AWS Marketplace

Everyone should have access to the tools and technologies that enable MLOps best practices. Machine learning has become an essential resource for companies of all shapes and sizes, from small startups to large enterprises. We continue to see customers leverage cloud providers like Amazon Web Services (AWS) as a trusted platform to discover, purchase and deploy AI solutions.

For this reason, we are excited to announce the launch of our public listing of WhyLabs on the AWS Marketplace. AWS customers worldwide can now quickly deploy the WhyLabs AI Observatory to monitor, understand, and debug their machine learning models deployed in AWS.

Data scientists and machine learning engineers can start using the WhyLabs AI Observatory in minutes by signing up for a free account through the Marketplace Listing. Their first model is monitored for free and, thanks to AWS Marketplace, they never have to talk to a salesperson or enter credit card information.

The AI Observatory makes model monitoring and AI observability easy, ensuring that users can be certain about the performance of the ML models they deploy. By integrating with the open source data profiling library whylogs, WhyLabs is able to ensure that sensitive data never leaves our customers’ VPCs. And because the AI Observatory is a SaaS offering, users don’t have to worry about any hidden costs or managing any infrastructure.

What’s on offer

With the AWS Marketplace offering, users can get access to the full WhyLabs AI observatory offering, including:

  • Perpetually free access for their first monitored ML model
  • Automated drift monitoring and anomaly detection
  • An interactive UI for debugging issues with data pipelines and ML models
  • A suite of notification integrations, including email, Slack, PagerDuty, etc
  • A single pane of glass for monitoring all machine learning models, regardless of how or where they are deployed

How to get started

Signing up for the AI Observatory through AWS Marketplace is easy:

Start off by logging into the AWS account that you want to use for payment. Go to (or search for “whylabs” in the AWS Marketplace console) and click the orange “Continue to Subscribe” button:


  1. Select the number of models you want to sign up for.
  2. Click Create Contract.
  3. Select Pay Now.
  4. Click Setup Your Account, which opens up the WhyLabs AI Observatory in a new tab.
  5. Create an account with any email address. It doesn’t have to be the one associated with your AWS account.

You’ll end up in the Observatory with a new model waiting for you to upload profiles to. You’ll be able to create new models up to the limit you signed up for.

If you want more models or support than we have listed in our marketplace listing, contact us at [email protected]  to set up a private offer.

Other posts

Model Monitoring for Financial Fraud Classification

Model monitoring is helping the financial services industry avoid huge losses caused by performance degradation in their fraud transaction models.

Data and ML Monitoring is Easier with whylogs v1.1

The release of whylogs v1.1 brings many features to the whylogs data logging API, making it even easier to monitor your data and ML models!

Robust & Responsible AI Newsletter - Issue #3

Every quarter we send out a roundup of the hottest MLOps and Data-Centric AI news including industry highlights, what’s brewing at WhyLabs, and more.

Data Quality Monitoring in Apache Airflow with whylogs

To make the most of whylogs within your existing Apache Airflow pipelines, we’ve created the whylogs Airflow provider. Using an example, we’ll show how you can use whylogs and Airflow to make your workflow more responsible, scalable, and efficient.

Data Logging with whylogs: Profiling for Efficiency and Speed

Rather than sampling data, whylogs captures snapshots of the data making it fast and efficient for data logging, even if your datasets scale to larger sizes.

Data Quality Monitoring for Kafka, Beyond Schema Validation

Data quality mapped to a schema registry or data type validation is a good start, but there are a few things most data application owners don’t think about. We explore error scenarios beyond schema validation and how to mitigate them.

Data + Model Monitoring with WhyLabs: simple, customizable, actionable

The new monitoring system maximizes the helpfulness of alerts and minimizes alert fatigue, so users can focus on improving their models instead of worrying about them in production...

A Solution for Monitoring Image Data

A breakdown of how to monitor unstructured data such as images, the types of problems that threaten computer vision systems, and a solution for these challenges.

How to Validate Data Quality for ML Monitoring

Data quality is one of the most important considerations for machine learning applications—and it's one of the most frequently overlooked. We explore why it’s an essential step in the MLOps process and how to check your data quality with whylogs.
pre footer decoration
pre footer decoration
pre footer decoration

Run AI With Certainty

Book a demo