AWS Machine Learning Blog

Category: Artificial Intelligence

Using Amazon SageMaker with Amazon Augmented AI for human review of Tabular data and ML predictions

Tabular data is a primary method to store data across multiple industries, including financial, healthcare, manufacturing, and many more. A large number of machine learning (ML) use cases deal with traditional structured or tabular data. For example, a fraud detection use case might be tabular inputs like a customer’s account history or payment details to […]

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Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast

Amazon 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 wide variety of use cases, including estimating product demand, supply chain optimization, resource planning, energy demand forecasting, and computing cloud infrastructure usage. With Forecast, there are no […]

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Pioneering personalized user experiences at StockX with Amazon Personalize

This is a guest post by Sam Bean and Nic Roberts II at StockX. In their own words, “StockX is a Detroit startup company revolutionizing ecommerce with a unique Bid/Ask marketplace—our platform models the New York Stock Exchange and treats goods like sneakers and streetwear as high-value, tradable commodities. With a transparent market experience, StockX […]

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Multi-GPU distributed deep learning training at scale with Ubuntu18 DLAMI, EFA on P3dn instances, and Amazon FSx for Lustre

AWS Deep Learning AMI (Ubuntu 18.04) is optimized for deep learning on EC2 Accelerated Computing Instance types, allowing you to scale out to multiple nodes for distributed workloads more efficiently and easily. It has a prebuilt Elastic Fabric Adapter (EFA), Nvidia GPU stack, and many deep learning frameworks (TensorFlow, MXNet, PyTorch, Chainer, Keras) for distributed […]

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Introducing Amazon SageMaker Components for Kubeflow Pipelines

Today we’re announcing Amazon SageMaker Components for Kubeflow Pipelines. This post shows how to build your first Kubeflow pipeline with Amazon SageMaker components using the Kubeflow Pipelines SDK. Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines.  Kubeflow Pipelines is an add-on to Kubeflow that lets […]

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Alexa uses Amazon Translate to reach more international customers

Amazon Alexa is available in 15 locales and eight languages. To understand and respond in different languages, Alexa needs to learn new grammar rules, and the content that powers Alexa needs to be translated to new languages. Additionally, Alexa needs to learn about country-specific topics, such as new soccer leagues, regional celebrities, and important historical […]

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Amazon Textract is now SOC and ISO compliant

You can now use Amazon Textract, a machine learning (ML) service that quickly and easily extracts text and data from forms and tables in scanned documents, for workloads that are subject to Service Organization Control (SOC) compliance and International Organization for Standardization (ISO) compliance. This launch builds upon the existing portfolio of AWS ML services […]

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Creating a music genre model with your own data in AWS DeepComposer

AWS DeepComposer is an educational AWS service that teaches generative AI and uses Generative Adversarial Networks (GANs) to transform a melody that you provide into a completely original song. With AWS DeepComposer, you can use one of the pre-trained music genre models (such as Jazz, Rock, Pop, Symphony, or Jonathan-Coulton) or train your own. As […]

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Designing human review workflows with Amazon Translate and Amazon Augmented AI

The world is becoming smaller as many businesses and organizations expand globally. As businesses expand their reach to wider audiences across different linguistic groups, their need for interoperability with multiple languages increases exponentially. Most of the industry work is manual, slow, and expensive human effort, with many industry verticals struggling to find a scalable, reliable, […]

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Implementing hyperparameter optimization with Optuna on Amazon SageMaker

Preferred Networks (PFN) released the first major version of their open-source hyperparameter optimization (HPO) framework Optuna in January 2020, which has an eager API. This post introduces a method for HPO using Optuna and its reference architecture in Amazon SageMaker. Amazon SageMaker supports various frameworks and interfaces such as TensorFlow, Apache MXNet, PyTorch, scikit-learn, Horovod, Keras, […]

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