The WhyLogs Blog

Ensuring data quality should be among your top priorities when developing an ML pipeline. In this article we’ll show how whylogs constraints with Github Actions can help with data validation, as a key component in ensuring data quality.

This month three members of the WhyLabs team are speaking at the Data and AI Summit. In this post you find descriptions and links to the talk by Alessya Visnjic, Leandro Almeida, and Andy Dang.

Evaluating the quality of data in the Kafka stream is a non-trivial task due to large volumes of data and latency requirements. This is an ideal job for whylogs, an open-source package for Python or Java that uses Apache DataSketches to monitor and detect statistical anomalies in streaming data.

Machine learning (ML) data is big and messy. Organizations have increasingly adopted RAPIDS and cuML to help their teams run experiments faster and achieve better model performance on larger datasets.

It's hard to overstate the importance of monitoring data quality in ML pipelines. In this post we will explore an elegant solution with whylogs and MLflow, which allows for a more informed analysis of model performance.

In traditional software, logging and instrumentation have been adopted as standard practice to create transparency and to make sense of the health of a complex system. When it comes to AI applications, the lack of tools and standardized approaches mean that logging is often spotty and incomplete.

As companies across industries adopt AI applications in order to improve products and stay competitive, very few have seen a return on their investments. That’s because AI operations are expensive...

Today, we are excited to announce WhyLabs, a company that empowers AI practitioners to reap the benefits of AI without the spectacular failures that so often make the news.