AWS Machine Learning Blog

Amazon Forecast now supports the generation of forecasts at a quantile of your choice

We are happy to announce that Amazon Forecast can now generate forecasts at a quantile of your choice. Launched at re:Invent 2018, and generally available since Aug 2019, Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. Forecast is applicable in a […]

Save on inference costs by using Amazon SageMaker multi-model endpoints

Businesses are increasingly developing per-user machine learning (ML) models instead of cohort or segment-based models. They train anywhere from hundreds to hundreds of thousands of custom models based on individual user data. For example, a music streaming service trains custom models based on each listener’s music history to personalize music recommendations. A taxi service trains […]

Automating financial decision making with deep reinforcement learning

Machine learning (ML) is routinely used in every sector to make predictions. But beyond simple predictions, making decisions is more complicated because non-optimal short-term decisions are sometimes preferred or even necessary to enable long-term, strategic goals. Optimizing policies to make sequential decisions toward a long-term objective can be learned using a family of ML models […]

Building an NLP-powered search index with Amazon Textract and Amazon Comprehend

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Organizations in all industries have a large number of physical documents. It can be difficult to extract text from a scanned document when it contains formats such as tables, forms, paragraphs, and check boxes. Organizations have been addressing these problems […]

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 […]

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 […]

Engage listeners with Amazon Polly’s Conversational speaking style voices

Update: As of June 28, 2021, the Conversational speaking-style version of Matthew and Joanna voices is now the default for both NTTS voices. SSML syntax is no longer required to trigger Conversational NTTS quality. For more details, see Polly documentation. All voices are unique, yet speakers tend to adjust their delivery, or speaking style, according […]

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 […]

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 […]

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 […]