Deploying machine learning models as serverless APIs

Favorite Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like

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Shared by AWS Machine Learning April 2, 2020

Increasing performance and reducing the cost of MXNet inference using Amazon SageMaker Neo and Amazon Elastic Inference

Favorite When running deep learning models in production, balancing infrastructure cost versus model latency is always an important consideration. At re:Invent 2018, AWS introduced Amazon SageMaker Neo and Amazon Elastic Inference, two services that can make models more efficient for deep learning. In most deep learning applications, making predictions using

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Shared by AWS Machine Learning March 31, 2020

AWS delivers sessions online at NVIDIA GTC Digital

Favorite Starting Tuesday, March 24, 2020, NVIDIA GTC Digital is offering courses for you to learn AWS best practices to accomplish your ML goals faster and more easily. Registration is free, so register now. The following sessions are available from AWS: S22492: Train BERT in One Hour Using Massive Cloud

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Shared by AWS Machine Learning March 25, 2020

Building a trash sorter with AWS DeepLens

Favorite In this blog post, we show you how to build a prototype trash sorter using AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning in a fun, hands-on way. This prototype trash sorter project teaches you how to train image classification models with

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Shared by AWS Machine Learning March 24, 2020

Investigating performance issues with Amazon CodeGuru Profiler

Favorite Amazon CodeGuru (Preview) analyzes your application’s performance characteristics and provides automatic recommendations on how to improve it. Amazon CodeGuru Profiler provides interactive visualizations to show you where your application spends its time. These flame graphs are a powerful tool to help you troubleshoot which code methods are causing delays

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Shared by AWS Machine Learning March 23, 2020