Getting started with Amazon SageMaker Feature Store

Favorite In a machine learning (ML) journey, one crucial step before building any ML model is to transform your data and design features from your data so that your data can be machine-readable. This step is known as feature engineering. This can include one-hot encoding categorical variables, converting text values

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Shared by AWS Machine Learning August 12, 2021

Run your TensorFlow job on Amazon SageMaker with a PyCharm IDE

Favorite As more machine learning (ML) workloads go into production, many organizations must bring ML workloads to market quickly and increase productivity in the ML model development lifecycle. However, the ML model development lifecycle is significantly different from an application development lifecycle. This is due in part to the amount

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Shared by AWS Machine Learning August 11, 2021

Attendee matchmaking at virtual events with Amazon Personalize

Favorite Amazon Personalize enables developers to build applications with the same machine learning (ML) technology used by Amazon.com for real-time personalized recommendations—no ML expertise required. Amazon Personalize makes it easy for developers to build applications capable of delivering a wide array of personalization experiences, including specific product recommendations, personalized product

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Shared by AWS Machine Learning August 10, 2021

Accenture promotes machine learning growth with world’s largest private AWS DeepComposer Battle of the Bands League

Favorite Accenture is known for pioneering innovative solutions to achieve customer success by using artificial intelligence (AI) and machine learning (ML) powered solutions with AWS services. To keep teams updated with latest ML services, Accenture seeks to gamify hands-on learning. One such event, AWS DeepComposer Battle of the Bands, hosted

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Shared by AWS Machine Learning August 7, 2021