AWS News Blog

Category: Artificial Intelligence

AWS DeepComposer – Now Generally Available With New Features

AWS DeepComposer, a creative way to get started with machine learning, was launched in preview at AWS re:Invent 2019. Today, I’m extremely happy to announce that DeepComposer is now available to all AWS customers, and that it has been expanded with new features. A primer on AWS DeepComposer If you’re new to AWS DeepComposer, here’s […]

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AWS Named as a Leader in Gartner’s Magic Quadrant for Cloud AI Developer Services

Last week I spoke to executives from a large AWS customer and had an opportunity to share aspects of the Amazon culture with them. I was able to talk to them about our Leadership Principles and our Working Backwards model. They asked, as customers often do, about where we see the industry in the next […]

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Now available in Amazon Transcribe: Automatic Redaction of Personally Identifiable Information

Launched at AWS re:Invent 2017, Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for AWS customers to add speech-to-text capabilities to their applications. At the time of writing, Transcribe supports 31 languages, 6 of which can be transcribed in real-time. A popular use case for Transcribe is the automatic transcription of […]

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Registration for Amazon re:MARS 2020 is 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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Alejandra’s Top 5 Favorite re:Invent🎉 Launches of 2019

While re:Invent 2019 may feel well over, I’m still feeling elated and curious about several of the launches that were announced that week. Is it just me, or did some of the new feature announcements seem to bring us closer to the Scifi worlds (i.e. AWS WaveLength anyone? and don’t get me started on Amazon […]

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New – Amazon Comprehend Medical Adds Ontology Linking

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights in unstructured text. It is very easy to use, with no machine learning experience required. You can customize Comprehend for your specific use case, for example creating custom document classifiers to organize your documents into your own categories, or custom […]

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Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning

Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML). We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, […]

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Amazon SageMaker Debugger – Debug Your Machine Learning Models

Today, we’re extremely happy to announce Amazon SageMaker Debugger, a new capability of Amazon SageMaker that automatically identifies complex issues developing in machine learning (ML) training jobs. Building and training ML models is a mix of science and craft (some would even say witchcraft). From collecting and preparing data sets to experimenting with different algorithms […]

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Amazon SageMaker Model Monitor – Fully Managed Automatic Monitoring For Your Machine Learning Models

Today, we’re extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear. The first thing I learned when I started working with data is that there is no such thing as paying too much […]

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Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation

Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure. Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.: Converting […]

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