The WhyLabs Blog

Running and monitoring distributed ML systems can be challenging. Fortunately, Ray makes parallelizing Python processes easy, and the open source whylogs enables users to monitor ML models in production, even if those models are running in a distributed environment.

In this blog post, we will dive into the WhyLabs AI Observatory, a data and ML monitoring and observability platform, and show how it complements Amazon SageMaker.

In this article, we deploy a Flask application for pattern recognition based on the well-known Iris Dataset. For the application monitoring, we’ll explore the free, starter edition of the WhyLabs Observability Platform in order to set up our own model monitoring dashboard.

SEATTLE, November 4, 2021 — WhyLabs, the leading provider of observability for AI and data applications announced today the close of a $10 million Series A co-led by Defy Partners and Andrew Ng’s AI Fund.

The AI community is experiencing an outbreak of concerns about the robustness and reliability of AI systems, but observability is the mechanism for creating a feedback loop between the ML pipeline and human operators that builds trust and transparency.

Concept drifts can originate in different stages of your data pipeline, even before the data collection itself. In this article, we’ll show how whylogs can help you monitor your machine learning system’s data ingestion pipeline by enabling concept drift detection, specifically for image data.

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.

Fire up your MLOps with a scalable, lightweight, open source data logging library Co-author: Bernease Herman We are thrilled to announce the open-source package whylogs. It enables data logging for any ML/AI pipeline in a few lines of code. Data logging is a critic...