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Amazon CodeGuru (Preview) is a service launched at AWS re:Invent 2019 that analyzes the performance characteristics of your application and provides automatic recommendations on ways to improve. It does this by profiling your application’s runtime (with CodeGuru Profiler) and by automatically reviewing source code changes (with CodeGuru Reviewer). For more information, see What Is Amazon […]
Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating product demand, energy demand, workforce planning, computing cloud infrastructure usage, traffic demand, supply chain optimization, and financial planning. Forecast is […]
The AWS Machine Learning Research Awards (MLRA) provides unrestricted cash funds and AWS Promotional Credits to academics to advance the frontiers of machine learning (ML) and its applications. MLRA is pleased to announce winners for its 2019 Q2/Q3 call-for-proposal cycles: Mohit Bansal, University of North Carolina Chapel Hill, Auto-Adversarial Training to Make Dialogue Systems Robust […]
AWS introduced the Machine Learning (ML) Solutions Lab a little over two years ago to connect our machine learning experts and data scientists with AWS customers. Our goal was to help our customers solve their most pressing business problems using ML. We’ve helped our customers increase fraud detection rates, improved forecasting and predictions for more […]
Amazon SageMaker Operators for Kubernetes allows you to augment your existing Kubernetes cluster with SageMaker hosted endpoints. Machine learning inferencing requires investment to create a reliable and efficient service. For an XGBoost model, developers have to create an application, such as through Flask that will load the model and then run the endpoint, which requires […]
As machine learning (ML) becomes a larger part of companies’ core business, there is a greater emphasis on reducing the time from model creation to deployment. In November of 2019, AWS released the AWS Step Functions Data Science SDK for Amazon SageMaker, an open-source SDK that allows developers to create Step Functions-based machine learning workflows […]
You have many choices for building, training, and deploying machine learning (ML) models. Weighing the financial considerations of different cloud solutions requires detailed analysis. You must consider the infrastructure, operational, and security costs for each step of the ML workflow, as well as the size and expertise of your data science teams. The Total Cost […]
The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually—3% of the nation’s $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses […]
March 2025: This blog post was reviewed and updated for accuracy Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with no prior machine learning experience. AWS is pleased to announce that Amazon […]
Enterprises often rely on unique identifiers to look up information on accounts or events. For example, airlines use confirmation codes to locate itineraries, and insurance companies use policy IDs to retrieve policy details. In customer support, these identifiers are the first level of information necessary to address customer requests. Identifiers are typically a combination of […]
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