AWS Machine Learning Blog

Users today don’t expect to be tied to a desktop computer. They want to interact with systems on the go, in a variety of ways that are convenient to them. This means that people often turn to mobile devices and interact with applications and systems while multi-tasking. Users might not even touch their mobile device while operating the apps they use, particularly when they are in a vehicle, or when they are actively engaged in another activity. In home environments, this “hands-free” capability is facilitated by voice activation systems.

Business users now aspire to the same experience in a business environment. They want to operate the applications and systems that they use in their daily work tasks using voice control, just like they do at home. Imagine how much simpler daily work tasks would be. However, adding voice controls to systems has not been easy. Voice integration can be a very involved project, even for skilled developers. Moreover, today’s business users want to solve their own tactical and strategic business problems by building “low code/no code” apps. Plus, business users want these apps to follow the same end-user requirements we mentioned earlier: They need to be able to be used on the go anywhere, anytime, hands-free.

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. In Part 1 of this blog post series, we […]

Amazon SageMaker now supports a new HTTP header for the InvokeEndpoint API action called CustomAttributes which can be used to provide additional information about an inference request or response. Amazon SageMaker strips all POST headers except those supported by the InvokeEndpoint API action and you can use the CustomAttributes header to pass custom information such […]

In this two-part blog post series we explore how to implement visual search using Amazon SageMaker and AWS DeepLens. In Part 1, we’ll take a look at how visual search works, and use Amazon SageMaker to create a model for visual search. We’ll also use Amazon SageMaker to build a fast index containing reference items to be searched.

In this blog post, I’ll show you how to perform exploratory analysis on massive corporate data sets in Amazon SageMaker. From your Jupyter notebook running on Amazon SageMaker, you’ll identify and explore several corporate datasets in the corporate data lake that seem interesting to you. You’ll discover that each contains a subset of the information you need. You’ll join them to extract the interesting information, then continue analyzing and visualizing your data in your Amazon SageMaker notebook, in a seamless experience.

Despite numerous cybersecurity efforts, phishing attacks are still on the rise. Phishing is a form of fraud where perpetrators pretend to be reputable companies and attempt to get individuals to reveal personal information, such as passwords and credit card numbers. It’s the most common social tactic.  93 percent of all breaches today start with phishing […]

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications. We’re excited to announce the availability of a new feature called channel identification, which allows users to process multi-channel audio files and retrieve a single transcript annotated with respective channel labels.

In this post, I create an intent in Amazon Lex for a translation action. This intent prompts the user for a source and target language, for example English to Spanish, followed by a text string to translate. Users are free to switch languages at any time during the interaction with Amazon Lex. The solution in the following illustration makes full use of Serverless Computing technologies to enable seamless scaling to thousands of users without the need for further engineering effort.

fast.ai, a research lab dedicated to making deep learning more accessible, has announced that they successfully trained the ResNet-50 deep learning model on a million images in 18 minutes using 16 Amazon EC2 P3.16xlarge instances. They accomplished this milestone by spending just $40. This new speed record illustrates how you can drastically cut down the training times for deep learning models, enabling you to bring your innovations to market faster and at a lower cost.

This is a guest post by Rob Whelan, Solutions Architect at Relus Cloud. When people ask me what impact artificial intelligence (AI) will have in the enterprise, I like to say that AI will relieve people from doing repetitive things. We’re not wired to do mindless tasks over and over—but computers are. That’s why self-driving […]