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Fundamentals of ML observability
Metrics, feature importance and more
We’re excited ???? to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Introduction and agenda >
Amazon SageMaker is a fully managed machine learning service offered by AWS (Amazon Web Services) that allows data scientists and machine learning engineers to easily create, train, and deploy ML models. It offers a seamless experience by integrating Jupyter notebooks, built-in algorithms, model training infrastructure, and real-time prediction capabilities.
Key features of Amazon SageMaker include:
Amazon SageMaker provides SageMaker Model Monitor, a service that lets you monitor the effectiveness and performance of machine learning models in production.
This is part of a series of articles about Machine Learning Models.
Amazon SageMaker Model Monitor helps detect and diagnose model quality issues, data drift, and concept drift, which are common challenges in machine learning model deployment. It enables continuous monitoring of production models and sends alerts when issues arise, allowing for prompt corrective action.
SageMaker Model Monitor works by analyzing the input and output data of deployed models and comparing them to the training data. It employs statistical analysis and machine learning techniques to identify anomalies and deviations from expected behavior. You can configure it to track specific metrics and set up alerts based on defined thresholds.
Key features of SageMaker Model Monitor include:
Learn more in our detailed guide to model monitoring
Using Amazon SageMaker Model Monitor in your machine learning pipeline provides several advantages for both development teams and businesses:
Data quality is crucial for maintaining the accuracy and effectiveness of machine learning models. Amazon SageMaker Model Monitor provides a comprehensive solution for automatically monitoring input data integrity.
Here is a general process for working with SageMaker Model Monitor to analyze data quality:
It is important to continuously assess model performance after deployment. Amazon SageMaker Model Monitor tracks various metrics and compares them against predefined thresholds:
Bias drift occurs when model predictions become biased towards specific demographics or subgroups. Model Monitor helps detect and mitigate bias drift by continuously monitoring key fairness metrics:
A shift in the distribution of real-time data for models in production can lead to a corresponding shift in feature attribution values, similar to how it might cause a drift in bias when monitoring bias metrics. You can use Amazon SageMaker’s Clarify feature track predictions for feature attribution drift. Here is a general process for working with feature attribution drift:
Aporia’s cutting-edge ML observability platform offers seamless integration with AWS SageMaker models, providing a powerful alternative to SageMaker Model Monitor. This innovative solution simplifies the process of monitoring and maintaining machine learning models by automatically generating advanced custom monitoring templates tailored to your specific use case.
In addition, Aporia’s platform allows for swift anomaly detection, drift monitoring, and performance analysis, ensuring your SageMaker models continue to deliver accurate predictions and maintain optimal performance. By leveraging Aporia’s user-friendly interface and advanced analytics, data scientists and ML engineers can easily gain insights into their models’ behavior, streamline debugging, and efficiently address any performance issues, all without the need for extensive manual intervention or code modification.
Aporia empowers organizations with key features and tools to ensure high model performance and Responsible AI:
To get a hands-on feel for Aporia’s advanced model monitoring and deep model visualization tools, we recommend: