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Favorite 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
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Shared by AWS Machine Learning July 17, 2021
Favorite 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
Read More Shared by AWS Machine Learning July 16, 2021
Favorite 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,
Read More Shared by AWS Machine Learning July 16, 2021
Favorite 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
Read More Shared by AWS Machine Learning July 16, 2021
Favorite Anomaly detection is the process of identifying items, events, or occurrences that have different characteristics from the majority of the data. It has many applications in various fields, like fraud detection for credit cards, insurance, or healthcare; network intrusion detection for cybersecurity; KPI metrics monitoring for critical systems; and
Read More Shared by AWS Machine Learning July 14, 2021
Favorite Defect detection during manufacturing processes is a vital step to ensure product quality. The timely detection of faults or defects and taking appropriate actions are essential to reduce operational and quality-related costs. According to Aberdeen’s research, “Many organizations will have true quality-related costs as high as 15 to 20
Read More Shared by AWS Machine Learning July 13, 2021
Favorite 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
Read More Shared by AWS Machine Learning July 13, 2021
Favorite 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
Read More Shared by AWS Machine Learning July 13, 2021
Favorite In this post, we show you how to use Amazon Rekognition and AWS DeepLens to detect, and analyze occupancy in a retail business to optimize workforce utilization. Retailers often need to make decisions to improve the in-store customer experience through personnel management. Having too few or too many employees
Read More Shared by AWS Machine Learning July 9, 2021
Favorite 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,
Read More Shared by AWS Machine Learning July 9, 2021