Favorite Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning

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Shared by AWS Machine Learning March 11, 2020

Favorite The AWS DeepRacer League is the world’s first autonomous racing league and is open to anyone. In 2019, tens of thousands of developers from around the world took part in a race against the clock to see who had the fastest model. The competition ended with SOLA from Japan

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Shared by AWS Machine Learning March 3, 2020

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

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

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

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Shared by AWS Machine Learning February 20, 2020

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

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Shared by AWS Machine Learning February 19, 2020

Favorite AWS introduced the Machine Learning (ML) Solutions Lab a little over two years ago to connect our machine learning experts and data scientists with AWS customers.  Our goal was to help our customers solve their most pressing business problems using ML. We’ve helped our customers increase fraud detection rates,

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Shared by AWS Machine Learning February 18, 2020

Favorite Amazon SageMaker Operators for Kubernetes allows you to augment your existing Kubernetes cluster with SageMaker hosted endpoints. Machine learning inferencing requires investment to create a reliable and efficient service. For an XGBoost model, developers have to create an application, such as through Flask that will load the model and

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Shared by AWS Machine Learning February 15, 2020

Favorite As machine learning (ML) becomes a larger part of companies’ core business, there is a greater emphasis on reducing the time from model creation to deployment. In November of 2019, AWS released the AWS Step Functions Data Science SDK for Amazon SageMaker, an open-source SDK that allows developers to

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Shared by AWS Machine Learning February 13, 2020

Favorite You have many choices for building, training, and deploying machine learning (ML) models. Weighing the financial considerations of different cloud solutions requires detailed analysis. You must consider the infrastructure, operational, and security costs for each step of the ML workflow, as well as the size and expertise of your

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Shared by AWS Machine Learning February 12, 2020