A Primer on Cost Saving Strategies for Deploying Models on the Cloud

How to think about cost savings for machine learning models Introduction The rise of organizations attempting to deploy and train custom Machine Learning models for specific purposes (such as demand forecasting, fraud detection, and more) has led to an increase in dependency on third-party cloud providers to host these models for training and inference purposes […]

Data Quality Monitoring in AWS SageMaker

First things first, what is data quality monitoring? Data quality monitoring for machine learning can generally be thought of from two perspectives. One perspective is that of traditional data-engineering. This type of monitoring is concerned with the “physical” characteristics of the data and ensuring they are what you expect them to be. It involves criteria […]

Remote Development in Sagemaker Studio with VS Code

Disclaimer about Changes to Sagemaker Studio As of Nov. 30 2023, there have been major changes to Sagemaker Studio. Existing customers of Sagemaker Studio will get the default experience now called Sagemaker Studio Classic — this is the Studio experience this article was written for. New Sagemaker Studio customers (and existing customers that choose to […]

Boost AI Fairness and Explainability with Amazon SageMaker Clarify

From hiring decisions to loan approvals and even healthcare recommendations, machine learning (ML) impacts our lives daily. Fairness and explainability are crucial in this context. Fairness means data is balanced, and model predictions are fair across groups. Checking for fairness ensures that negative outcomes are fair across all groups, such as age or gender. Explainability […]