blog bg left
Back to Blog

Monitor your SageMaker model with WhyLabs

As the real-world changes, machine learning models degrade in their ability to accurately represent it, resulting in model performance degradation. That’s why it’s important for data scientists and machine learning engineers to support models with tools that provide ML monitoring and observability, thereby preventing that performance degradation. 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.

Amazon SageMaker is incredibly powerful for training and deploying machine learning models at scale. WhyLabs allows you to monitor and observe your machine learning model, ensuring that it doesn’t suffer from performance degradation and continues to provide value to your business. In this blog post, we’re going to demonstrate how to use WhyLabs to identify training-serving skew in a computer vision example for a model trained and deployed with SageMaker. WhyLabs is unique in its ability to monitor computer vision models and image data; whylogs library is able to extract features and metadata from images as described in “Detecting Semantic Drift within Image Data”. The ability to create profiles based on images means that users can identify differences between training data and serving data and understand whether they need to retrain their models...

Continue reading on the AWS Startup Blog website

Other posts

Model Monitoring for Financial Fraud Classification

Model monitoring is helping the financial services industry avoid huge losses caused by performance degradation in their fraud transaction models.

Data and ML Monitoring is Easier with whylogs v1.1

The release of whylogs v1.1 brings many features to the whylogs data logging API, making it even easier to monitor your data and ML models!

Robust & Responsible AI Newsletter - Issue #3

Every quarter we send out a roundup of the hottest MLOps and Data-Centric AI news including industry highlights, what’s brewing at WhyLabs, and more.

Data Quality Monitoring in Apache Airflow with whylogs

To make the most of whylogs within your existing Apache Airflow pipelines, we’ve created the whylogs Airflow provider. Using an example, we’ll show how you can use whylogs and Airflow to make your workflow more responsible, scalable, and efficient.

Data Logging with whylogs: Profiling for Efficiency and Speed

Rather than sampling data, whylogs captures snapshots of the data making it fast and efficient for data logging, even if your datasets scale to larger sizes.

Data Quality Monitoring for Kafka, Beyond Schema Validation

Data quality mapped to a schema registry or data type validation is a good start, but there are a few things most data application owners don’t think about. We explore error scenarios beyond schema validation and how to mitigate them.

Data + Model Monitoring with WhyLabs: simple, customizable, actionable

The new monitoring system maximizes the helpfulness of alerts and minimizes alert fatigue, so users can focus on improving their models instead of worrying about them in production...

A Solution for Monitoring Image Data

A breakdown of how to monitor unstructured data such as images, the types of problems that threaten computer vision systems, and a solution for these challenges.

How to Validate Data Quality for ML Monitoring

Data quality is one of the most important considerations for machine learning applications—and it's one of the most frequently overlooked. We explore why it’s an essential step in the MLOps process and how to check your data quality with whylogs.
pre footer decoration
pre footer decoration
pre footer decoration

Run AI With Certainty

Book a demo
loading...