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

Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker

Favorite Amazon SageMaker enables organizations to build, train, and deploy machine learning models. Consumer-facing organizations can use it to enrich their customers’ experiences, for example, by making personalized product recommendations, or by automatically tailoring application behavior based on customers’ observed preferences. When building such applications, one key architectural consideration is

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Shared by AWS Machine Learning March 13, 2020

Using DeepChem with Amazon SageMaker for virtual screening

Favorite Virtual screening is a computational methodology used in drug or materials discovery by searching a vast amount of molecules libraries to identify the structures that are most likely to show the target characteristics. It is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available

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Shared by AWS Machine Learning February 28, 2020

Optimizing application performance with Amazon CodeGuru Profiler

Favorite Amazon CodeGuru (Preview) is a service launched at AWS re:Invent 2019 that analyzes the performance characteristics of your application and provides automatic recommendations on ways to improve. It does this by profiling your application’s runtime (with CodeGuru Profiler) and by automatically reviewing source code changes (with CodeGuru Reviewer). For

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Shared by AWS Machine Learning February 27, 2020