Favorite 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

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Shared by AWS Machine Learning June 2, 2020

Favorite 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

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Shared by AWS Machine Learning May 30, 2020

Favorite 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,

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Shared by AWS Machine Learning May 29, 2020

Favorite At re:Invent 2019, AWS shared the fastest training times on the cloud for two popular machine learning (ML) models: BERT (natural language processing) and Mask-RCNN (object detection). To train BERT in 1 hour, we efficiently scaled out to 2,048 NVIDIA V100 GPUs by improving the underlying infrastructure, network, and ML framework. Today,

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Shared by AWS Machine Learning May 28, 2020

Favorite Managing the complete lifecycle of a deep learning project can be challenging, especially if you use multiple separate tools and services. For example, you may use different tools for data preprocessing, prototyping training and inference code, full-scale model training and tuning, model deployments, and workflow automation to orchestrate all

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Shared by AWS Machine Learning May 27, 2020

Favorite In recent years, AWS customers have been running machine learning (ML) on an increasing variety of datasets and data sources. Because a large percentage of organizational data is stored in relational databases such as Amazon Aurora, there’s a common need to make this relational data available for training ML

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Shared by AWS Machine Learning May 22, 2020

Favorite Developers, to help you advance your AI and machine learning (ML) skills with hands-on and engaging learning, the AWS Machine Learning Scholarship Program from Udacity is now open for enrollment. AWS and Udacity are collaborating to educate developers of all skill levels to expand their AWS ML expertise. In

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Shared by AWS Machine Learning May 20, 2020

Favorite AWS is excited to announce the general availability of Amazon SageMaker integration in QuickSight. You can now integrate your own Amazon SageMaker ML models with QuickSight to analyze the augmented data and use it directly in your business intelligence dashboards. As a business analyst, data engineer, or data scientist,

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Shared by AWS Machine Learning May 19, 2020

Favorite Veeva Systems is a provider of cloud-based software for the global life sciences industry, which offers products that serve multiple domains ranging from clinical, regulatory, quality, and more. Veeva’s Vault Platform manages both content and data in a single platform that allows you to deploy powerful applications that manage

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Shared by AWS Machine Learning May 16, 2020

Favorite As the world responds to the ongoing pandemic, it’s more important than ever to accurately access, consume, and analyze information related to COVID-19. Topics about the healthcare crisis permeate many dimensions of our personal and professional lives, through channels as diverse as news reporting, social media, business meetings, radio

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Shared by AWS Machine Learning May 16, 2020