The WhyLogs Blog

Our ideas and thoughts on how to run AI with certainty

Detecting Semantic Drift within Image Data: Monitoring Context-Full Data with whylogs

FEATURED POST

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.

OTHER POSTS

Don’t Let Your Data Fail You; Continuous Data Validation with whylogs and Github Actions

Don’t Let Your Data Fail You; Continuous Data Validation with whylogs and Github Actions

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.
WhyLabs' Data Geeks Unleashed

WhyLabs' Data Geeks Unleashed

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.
Integrating whylogs into your Kafka ML Pipeline

Integrating whylogs into your Kafka ML Pipeline

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.
Monitoring High-Performance Machine Learning Models with RAPIDS and whylogs

Monitoring High-Performance Machine Learning Models with RAPIDS and whylogs

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.
Streamlining data monitoring with whylogs and MLflow

Streamlining data monitoring with whylogs and MLflow

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.
Data Logging: Sampling versus Profiling

Data Logging: Sampling versus Profiling

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.
WhyLabs: The AI Observability Platform

WhyLabs: The AI Observability Platform

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...
Introducing WhyLabs, a Leap Forward in AI Reliability

Introducing WhyLabs, a Leap Forward in AI Reliability

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.

Run AI with Certainty

We take the pain out of model and data monitoring so that you spend less time
firefighting, and more time building models.

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