AWS Machine Learning Blog

Category: Artificial Intelligence

Lowering total cost of ownership for machine learning and increasing productivity with Amazon SageMaker

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 […]

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Flagging suspicious healthcare claims with Amazon SageMaker

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 […]

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Amazon Personalize can now use 10X more item attributes to improve relevance of recommendations

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 Personalize now supports ten times more item attributes for modeling in […]

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Capturing and validating alphanumeric identifiers in Amazon Lex

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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Registration for Amazon re:MARS is Now Open

Editor’s Note: We have been closely monitoring the situation with COVID-19, and after much consideration, we have made the decision to cancel re:MARS 2020. Our top priority is the well-being of our employees, customers, partners, and event attendees. Over the course of the coming weeks, we will explore other ways to engage the community. To […]

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Build a unique Brand Voice with Amazon Polly

AWS is pleased to announce a new feature in Amazon Polly called Brand Voice, a capability in which you can work with the Amazon Polly team of AI research scientists and linguists to build an exclusive, high-quality, Neural Text-to-Speech (NTTS) voice that represents your brand’s persona. Brand Voice allows you to differentiate your brand by […]

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Identifying worker labeling efficiency using Amazon SageMaker Ground Truth

A critical success factor in machine learning (ML) is the cleanliness and accuracy of training datesets. Training with mislabeled or inaccurate data can lead to a poorly performing model. But how can you easily determine if the  labeling team is  accurately labeling data? One way is to manually sift through the results one worker at […]

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Millennium Management: Secure machine learning using Amazon SageMaker

This is a guest post from Millennium Management. In their own words, “Millennium Management is a global investment management firm, established in 1989, with over 2,900 employees and $39.2 billion in assets under management as of August 2, 2019.” Millennium Management is comprised of a large number of specialized trading teams across the United States, […]

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Amazon Comprehend now supports multi-label custom classification

Amazon Comprehend is a fully managed natural language processing (NLP) service that enables text analytics to extract insights from the content of documents. Amazon Comprehend supports custom classification and enables you to build custom classifiers that are specific to your requirements, without the need for any ML expertise. Previously, custom classification supported multi-class classification, which is […]

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Building a business intelligence dashboard for your Amazon Lex bots

You’ve rolled out a conversational interface powered by Amazon Lex, with a goal of improving the user experience for your customers. Now you want to track how well it’s working. Are your customers finding it helpful? How are they using it? Do they like it enough to come back? How can you analyze their interactions […]

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