Category: Amazon Machine Learning

AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence), […]

Healthcare technology companies often turn to AWS to help them accelerate their clinical and business objectives. MEDHOST has provided enterprise information technology and electronic health record (EHR) solutions to full-service community hospitals for more than 35 years. Today, more than 1,000 healthcare facilities are partnering with MEDHOST and enhancing their patient care and operational excellence […]

For many enterprises, critical business information is often stored as unstructured data scattered across multiple content repositories. It is challenging for organizations to make this information available to users when they need it. It is also difficult to do so securely so that relevant information is available to the right users or user groups. Different […]

Amazon HealthLake is a new HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. Amazon HealthLake removes the heavy lifting of organizing, indexing, and structuring patient information to provide a complete view of the health of individual patients and entire patient populations in a secure, compliant, and auditable manner. With the […]

Amazon HealthLake is a new, HIPAA-eligible service designed to store, transform, query, and analyze health data at scale. You can bring your healthcare data into Amazon HealthLake using Fast Healthcare Interoperability Resources (FHIR) R4 APIs. If you don’t have your data in FHIR R4, Amazon has collaborated with industry experts to build Amazon HealthLake connectors […]

Last year, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart hosts 196 computer vision models, 64 natural language processing (NLP) models, 18 pre-built end-to-end solutions, and 19 example notebooks to help you get started with using […]

This post outlines the best practices for provisioning Amazon SageMaker Studio for data science teams and provides reference architectures and AWS CloudFormation templates to help you get started. We use AWS Service Catalog to provision a Studio domain and users. The AWS Service Catalog allows you to provision these centrally without requiring each user to […]

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model […]

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. In this post, we show you how to use […]