AWS Machine Learning Blog

Category: Artificial Intelligence

How AWS is putting machine learning in the hands of every developer and BI analyst

Today AWS announced new ways for you to easily add machine learning (ML) predictions to applications and business intelligence (BI) dashboards using relational data in your Amazon Aurora database and unstructured data in Amazon S3, by simply adding a few statements to your SQL (structured query language) queries and making a few clicks in Amazon […]

Read More

Amazon Transcribe now supports speech-to-text in 31 languages

We recently announced that Amazon Transcribe now supports transcription for audio and video for 7 additional languages including Gulf Arabic, Swiss German, Hebrew, Japanese, Malay, Telugu, and Turkish languages.  Using Amazon Transcribe, customers can now take advantage of 31 supported languages for transcription use cases such as improving customer service, captioning and subtitling, meeting accessibility requirements, and cataloging audio […]

Read More

Engage listeners with Amazon Polly’s Conversational speaking style voices

All voices are unique, yet speakers tend to adjust their delivery, or speaking style, according to their context and audience. Before Amazon Polly used Neural Text-to-Speech technology (NTTS) to build voices, TTS (Standard Text-to-Speech) voices couldn’t change their speech patterns to match any particular speaking style. When Amazon Polly introduced NTTS, Newscaster voices were launched […]

Read More

Announcing Amazon Rekognition Custom Labels

Today, Amazon Web Services (AWS) announced Amazon Rekognition Custom Labels, a new feature of Amazon Rekognition that enables customers to build their own specialized machine learning (ML) based image analysis capabilities to detect unique objects and scenes integral to their specific use case. For example, customers using Amazon Rekognition to detect machine parts from images […]

Read More

Designing conversational experiences with sentiment analysis in Amazon Lex

To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, “Sorry to hear you are having trouble.” Understanding sentiment is also useful in determining when you need to hand over the […]

Read More

Real-time music recommendations for new users with Amazon SageMaker

This is a guest post from Matt Fielder and Jordan Rosenblum at iHeartRadio. In their own words, “iHeartRadio is a streaming audio service that reaches tens of millions of users every month and registers many tens of thousands more every day.” Personalization is an important part of the user experience, and we aspire to give […]

Read More

Chaining Amazon SageMaker Ground Truth jobs to label progressively

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It can reduce your labeling costs by up to 70% using automatic labeling. This blog post explains the Amazon SageMaker Ground Truth chaining feature with a few examples and its potential in labeling your datasets. Chaining reduces time and cost significantly […]

Read More

Subtitling videos accurately and easily with CaptionHub and AWS

This is a guest post from Graham Pengelly, CTO, and James Jameson, the Commercial Lead, at CaptionHub. CaptionHub is a London-based company that focuses on video captioning and subtitling production for enterprise organizations. While the act of captioning—that is, taking video files and making sure the text on the screen reflects what’s being said accurately […]

Read More

Exploring images on social media using Amazon Rekognition and Amazon Athena

If you’re like most companies, you wish to better understand your customers and your brand image. You’d like to track the success of your marketing campaigns, and the topics of interest—or frustration—for your customers. Social media promises to be a rich source of this kind of information, and many companies are beginning to collect, aggregate, […]

Read More

Adding AI to your applications with ready-to-use models from AWS Marketplace

Machine learning (ML) lets enterprises unlock the true potential of their data, automate decisions, and transform their business processes to deliver exponential value to their customers. To help you take advantage of ML, Amazon SageMaker provides the ability to build, train, and deploy ML models quickly. Until recently, if you used Amazon SageMaker, you could […]

Read More