Controlling and auditing data exploration activities with Amazon SageMaker Studio and AWS Lake Formation

Favorite Highly-regulated industries, such as financial services, are often required to audit all access to their data. This includes auditing exploratory activities performed by data scientists, who usually query data from within machine learning (ML) notebooks. This post walks you through the steps to implement access control and auditing capabilities

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Shared by AWS Machine Learning December 22, 2020

Population health applications with Amazon HealthLake – Part 1: Analytics and monitoring using Amazon QuickSight

Favorite Healthcare has recently been transformed by two remarkable innovations: Medical Interoperability and machine learning (ML). Medical Interoperability refers to the ability to share healthcare information across multiple systems. To take advantage of these transformations, we launched a new HIPAA-eligible healthcare service, Amazon HealthLake, now in preview at re:Invent 2020.

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Shared by AWS Machine Learning December 19, 2020

Monitoring in-production ML models at large scale using Amazon SageMaker Model Monitor

Favorite Machine learning (ML) models are impacting business decisions of organizations around the globe, from retail and financial services to autonomous vehicles and space exploration. For these organizations, training and deploying ML models into production is only one step towards achieving business goals. Model performance may degrade over time for

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Shared by AWS Machine Learning December 18, 2020

KM accountabilities for the knowledge domain owners and SMEs

Favorite  I blogged earlier this week about the KM accountabilities for project managers. Here is the counterpart – the KM accountability for the knowledge domain owners. There are two dimensions to KM within a project based organisation. These are KM within individual projects, and KM across and between the projects.

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Shared by Nick Milton December 18, 2020