AWS Machine Learning

Financial institutions that extend credit face the dual tasks of evaluating the credit risk associated with each loan application and determining a threshold that defines the level of risk they are willing to take […]

With less than 40 days to re:Invent 2019, the excitement is building up and we are looking forward to seeing you all soon! Continuing our journey on artificial intelligence and machine learning, we are bringing a […]

Amazon Polly turns text into lifelike speech. In July 2019, AWS launched eight US English and three UK English voices in Neural Text-to-Speech (NTTS) technology, which delivers ground-breaking improvements in […]

Today AWS is pleased to announce that it is working with Facebook, Microsoft, and the Partnership on AI on the first Deepfakes Detection Challenge.  The competition, to which we are contributing up to $1 million […]

The AWS DeepRacer League is the world’s first autonomous racing league, open to anyone. Announced at re:Invent 2018, it puts machine learning in the hands of every developer in a fun and exciting way. Throughout 2 […]

The 2019 Major League Baseball (MLB) postseason is here after an exhilarating regular season in which fans saw many exciting new developments. MLB and Amazon Web Services (AWS) teamed up to develop and deliver […]

In daily conversations, you often jump back and forth between multiple topics. For example, when discussing a home improvement project related to new windows and curtains, you might have questions like, “How a […]

Today, Amazon Web Services (AWS) announced that Amazon Textract, a machine learning service that quickly and easily extracts text and data from scanned documents, is now eligible for healthcare workloads that […]

Building a highly accurate training dataset for your machine learning (ML) algorithm is an iterative process. It is common to review and continuously adjust your labels until you are satisfied that the labels […]

Ever been stumped by a question? Imagine you’re in a business review going over weekly numbers and someone asks, “What about expenses?” Your response might be, “I don’t know. I wasn’t prepared to have that discus […]

Amazon Textract is a machine learning service that makes it easy to extract text and data from virtually any document. Textract goes beyond simple optical character recognition (OCR) to also identify the contents […]

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. In November 2018, we added streaming transcriptions over […]

AWS offers a vast array of services that allow developers to build applications in the cloud. At the same time, Windows desktop applications can take advantage of these services as well. Today, we are releasing […]

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. Today, I am excited to announce a new education course, built in collaboration w […]

Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. When you deploy an ML model, […]

Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. Customer X has the following problem: They are about to release a new car model […]

Creating a global footprint and access to scale are one of the many best practices at AWS. By creating architectures that take advantage of that scale and also efficient data utilization (in both performance and […]

Amazon SageMaker supports all the popular deep learning frameworks, including TensorFlow. Over 85% of TensorFlow projects in the cloud run on AWS. Many of these projects already run in Amazon SageMaker. This is […]

This post offers a dive deep into how to use Amazon Elastic Inference with Amazon Elastic Kubernetes Service. When you combine Elastic Inference with EKS, you can run low-cost, scalable inference workloads with […]

After you’ve trained and exported a TensorFlow model, you can use Amazon SageMaker to perform inferences using your model. You can either:

Deploy your model to an endpoint to obtain real-time inferences from y […]