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What is WhyLabs#
WhyLabs is an AI observability platform that prevents data quality or model performance degradation by allowing you to monitor your data pipelines and machine learning models in production. If you deploy an ML model but don’t have visibility into its performance, you risk doing damage to your business due to model degradation resulting from things like data/concept drift, data corruption, schema changes and more. In many cases, data issues do not throw hard errors and can go undetected in data pipelines at a great cost to your business. With WhyLabs, you can prevent this performance degradation by monitoring your model/dataset with a platform that’s easy to use, privacy preserving, and cost efficient.
WhyLabs relies on profiles generated by the open source whylogs library to monitor the data flowing through your pipeline or being fed to your model. These profiles can also capture the predictions that a model generates, allowing you to monitor the performance of the model.
Profiles created via whylogs library contain a variety of statistics describing your dataset and vary depending on whether you’re profiling tabular data, text data, image data, etc. These profiles are generated locally so your actual data never leaves your environment.
Users can upload these profiles to the WhyLabs AI Observability Platform via our API which provides users with extensive monitoring and alerting capabilities right out of the box.
The WhyLabs approach to AI observability and monitoring is based on cutting edge research, but flexibility is a priority and users have plenty of options to customize their implementation to their needs.
To read more about WhyLabs, check out the WhyLabs Overview
What is whylogs#
whylogs is the open source standard for profiling data. whylogs automatically creates statistical summaries of datasets, called profiles, which imitate the logs produced by other software applications. The library was developed with the goal of bridging the data logging gap by providing profiling capabilities to capture data-specific logs. whylogs profiles are descriptive, lightweight, and mergeable, making them a natural fit for data logging applications. whylogs can generate logs from datasets stored in Python, Java, or Spark environments.
To read more about WhyLabs, check out the whylogs Overview
How to Navigate These Docs#
Our documentation contains conceptual explanations, technical specifications, and tutorials.
In this Overview section, you'll find conceptual explanations that give you context about the WhyLabs Platform and the open source whylogs library.
In the Use Cases section, you'll find tutorials that walk you through how to do various things with whylogs and WhyLabs, such as Generating Profiles or Checking Data Quality.
In the Integrations section, you'll find more tutorials that specify how to integrate with various other DataOps and MLOps tools, such as MLFlow and Databricks.
In the WhyLabs Platform section, you'll primarily find technical specifications of the WhyLabs platform, as well as some conceptual explanations of its features.
In the whylogs API section, you'll primarily find technical specifications of the whylogs library, as well as some conceptual explanations of its features.
