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Favorite The Earth’s climate is a highly complex, dynamic system. It is difficult to understand and predict how different climate variables interact. Finding causal relations in climate research today relies mostly on expensive and time-consuming model simulations. Fortunately, with the explosion in the availability of large-scale climate data and increasing
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Shared by AWS Machine Learning January 22, 2020
Favorite The Earth’s climate is a highly complex, dynamic system. It is difficult to understand and predict how different climate variables interact. Finding causal relations in climate research today relies mostly on expensive and time-consuming model simulations. Fortunately, with the explosion in the availability of large-scale climate data and increasing
Read More Shared by AWS Machine Learning January 22, 2020
Favorite Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. Amazon SageMaker and Apache MXNet on AWS can automate horizon picking using deep learning techniques. In this post, I use these services to build and train a custom deep-learning model for
Read More Shared by AWS Machine Learning January 21, 2020
Favorite Amazon Polly turns text into lifelike speech, which allows you to create voice-enabled applications. AWS is excited to announce the general availability of all standard voices in the Middle East (Bahrain) and Asia Pacific (Hong Kong) Regions. Customers in these Regions can now synthesize over 60 standard voices available
Read More Shared by AWS Machine Learning January 16, 2020
Favorite Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute
Read More Shared by AWS Machine Learning January 9, 2020
Favorite Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute
Read More Shared by AWS Machine Learning January 9, 2020
Favorite Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant
Read More Shared by AWS Machine Learning January 7, 2020
Favorite Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant
Read More Shared by AWS Machine Learning January 7, 2020
Favorite Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in texts. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; and understands how positive or negative the text is. For more information
Read More Shared by AWS Machine Learning December 25, 2019
Favorite As a product owner for a conversational interface, understanding and improving the user experience without the corresponding visibility or telemetry can feel like driving a car blindfolded. It is important to understand how users are interacting with your bot so that you can continuously improve the bot based on
Read More Shared by AWS Machine Learning December 20, 2019