Machine learning best practices in financial services

Favorite We recently published a new whitepaper, Machine Learning Best Practices in Financial Services, that outlines security and model governance considerations for financial institutions building machine learning (ML) workflows. The whitepaper discusses common security and compliance considerations and aims to accompany a hands-on demo and workshop that walks you through

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Shared by AWS Machine Learning August 10, 2020

The link between Data, Information and Knowledge – an example

Favorite The link between Data, Information and Knowledge is not as simple as the three being a linear progression. Knowledge is something you ADD to Data and Information, rather than something that arises FROM Information. As an illustration, consider the link between data, information and knowledge as they are involved

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Shared by Nick Milton August 6, 2020

Deploying your own data processing code in an Amazon SageMaker Autopilot inference pipeline

Favorite The machine learning (ML) model-building process requires data scientists to manually prepare data features, select an appropriate algorithm, and optimize its model parameters. It involves a lot of effort and expertise. Amazon SageMaker Autopilot removes the heavy lifting required by this ML process. It inspects your dataset, generates several

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Shared by AWS Machine Learning August 5, 2020

Safely deploying and monitoring Amazon SageMaker endpoints with AWS CodePipeline and AWS CodeDeploy

Favorite As machine learning (ML) applications become more popular, customers are looking to streamline the process for developing, deploying, and continuously improving models. To reliably increase the frequency and quality of this cycle, customers are turning to ML operations (MLOps), which is the discipline of bringing continuous delivery principles and

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Shared by AWS Machine Learning August 5, 2020

Improving speech-to-text transcripts from Amazon Transcribe using custom vocabularies and Amazon Augmented AI

Favorite Businesses and organizations are increasingly using video and audio content for a variety of functions, such as advertising, customer service, media post-production, employee training, and education. As the volume of multimedia content generated by these activities proliferates, businesses are demanding high-quality transcripts of video and audio to organize files,

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Shared by AWS Machine Learning August 4, 2020