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

Read More
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

Read More
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

Read More
Shared by AWS Machine Learning February 27, 2020

Automating your Amazon Forecast workflow with Lambda, Step Functions, and CloudWatch Events rule

Favorite Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating product demand, energy demand, workforce planning, computing cloud infrastructure usage, traffic demand, supply chain

Read More
Shared by AWS Machine Learning February 20, 2020

Winners of AWS Machine Learning Research Awards announced

Favorite The AWS Machine Learning Research Awards (MLRA) provides unrestricted cash funds and AWS Promotional Credits to academics to advance the frontiers of machine learning (ML) and its applications. MLRA is pleased to announce winners for its 2019 Q2/Q3 call-for-proposal cycles: Mohit Bansal, University of North Carolina Chapel Hill, Auto-Adversarial

Read More
Shared by AWS Machine Learning February 19, 2020