September 2020 Summaries

whylogs is an open-source data logging library that enables data logging for any ML/AI pipeline in a few lines of code. It provides essential insights into the health and performance of the pipeline, helping developers and data scientists to be aware of changes in the quality and consistency of their data. The library supports a wide range of ML data types, including complex inputs like images and video, and is designed for both interactive analysis and production settings. whylogs is massively scalable due to its static memory footprint and lightweight output, which minimizes storage costs. It also prevents overcollection of data to reduce costs, security vulnerabilities, and operational complexity. The library currently supports columnar data in Python and Java, with plans to add support for time series, text, and image data types, as well as more languages.

WhyLabs is an AI observability platform designed to help companies improve their AI applications. The platform enables organizations of all sizes to run AI with certainty by providing tools previously only available to tech giants. It focuses on data logging and monitoring, offering a solution called whylogs that efficiently handles massive amounts of data. The WhyLabs Platform integrates seamlessly with various cloud services and ML frameworks, supporting all deployment strategies. By creating a single pane of glass for all data quality and model health information, the platform allows users to track raw data, feature data, model predictions, and actuals across their entire AI application pipeline.

WhyLabs is a new company that aims to improve the reliability of AI applications by providing an observability platform. The founders experienced firsthand the challenges and difficulties associated with adopting AI, leading them to develop tools specifically for AI applications. They identified three core functions necessary for any AI operations solution: increasing the observability of AI applications, performing continuous data quality monitoring, and keeping all stakeholders informed about the behavior of the application. The WhyLabs platform enables observability in AI applications by instrumenting the end-to-end AI lifecycle with a lightweight logging agent that continuously collects vital information about the data flowing through each step.