AWS Machine Learning Blog

Category: Artificial Intelligence

Active learning workflow for Amazon Comprehend custom classification models – Part 1

Update Sep 2021: Amazon Comprehend has launched a suite of features for Comprehend Custom to enable continuous model improvements by giving developers the ability to version custom models, new training options for custom entity recognition models that reduce data preprocessing, ability to provide specific test sets during training, and live migration to new model endpoints. Refer to […]

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Introducing a new API to stop in-progress workflows in Amazon Forecast

Amazon Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at to developers as a fully managed service, removing the need to manage resources or rebuild your systems. To start generating forecasts through Forecast, you can follow three steps of […]

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The following diagram illustrates the architecture of the data processing and pipeline.

Multimodal deep learning approach for event detection in sports using Amazon SageMaker

Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they’re […]

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From the following confusion matrix, we can see that the model does a better job at predicting for class 0 than class 1.

Utilizing XGBoost training reports to improve your models

In 2019, AWS unveiled Amazon SageMaker Debugger, a SageMaker capability that enables you to automatically detect a variety of issues that may arise while a model is being trained. SageMaker Debugger captures model state data at specified intervals during a training job. With this data, SageMaker Debugger can detect training issues or anomalies by leveraging […]

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Integrating Amazon Polly with legacy IVR systems by converting output to WAV format

Amazon Web Services (AWS) offers a rich stack of artificial intelligence (AI) and machine learning (ML) services that help automate several components of the customer service industry. Amazon Polly, an AI generated text-to-speech service, enables you to automate and scale your interactive voice solutions, helping to improve productivity and reduce costs. You might face common […]

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Ripley is a Clearpath Robotics Husky equipped with two Universal Robotics UR5 arms.

Introducing Amazon SageMaker Reinforcement Learning Components for open-source Kubeflow pipelines

This blog post was co-authored by AWS and Max Kelsen. Max Kelsen is one of Australia’s leading Artificial Intelligence (AI) and Machine Learning (ML) solutions businesses. The company delivers innovation, directly linked to the generation of business value and competitive advantage to customers in Australia and globally, including Fortune 500 companies. Max Kelsen is also […]

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Analyzing open-source ML pipeline models in real time using Amazon SageMaker Debugger

Open-source workflow managers are popular because they make it easy to orchestrate machine learning (ML) jobs for productions. Taking models into productions following a GitOps pattern is best managed by a container-friendly workflow manager, also known as MLOps. Kubeflow Pipelines (KFP) is one of the Kubernetes-based workflow managers used today. However, it doesn’t provide all […]

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You already know how to use Amazon Athena to transform data in Amazon S3 using simple SQL commands

Translate, redact, and analyze text using SQL functions with Amazon Athena, Amazon Translate, and Amazon Comprehend

October 2021 Update (v0.3.0): Added support for Amazon Comprehend DetectKeyPhrases You have Amazon Simple Storage Service (Amazon S3) buckets full of files containing incoming customer chats, product reviews, and social media feeds, in many languages. Your task is to identify the products that people are talking about, determine if they’re expressing happy thoughts or sad […]

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The following diagram illustrates our solution architecture.

Setting up Amazon Personalize with AWS Glue

Data can be used in a variety of ways to satisfy the needs of different business units, such as marketing, sales, or product. In this post, we focus on using data to create personalized recommendations to improve end-user engagement. Most ecommerce applications consume a huge amount of customer data that can be used to provide […]

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Amazon Rekognition Custom Labels Community Showcase

In our Community Showcase, Amazon Web Services (AWS) highlights projects created by AWS Heroes and AWS Community Builders.  We worked with AWS Machine Learning (ML) Heroes and AWS ML Community Builders to bring to life projects and use cases that detect custom objects with Amazon Rekognition Custom Labels. The AWS ML community is a vibrant […]

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