AWS Machine Learning Blog
Category: Amazon SageMaker
Pruning machine learning models with Amazon SageMaker Debugger and Amazon SageMaker Experiments
In the past decade, deep learning has advanced many different areas, such as computer vision and natural language processing. State-of-the-art models now achieve near-human performance in tasks such as image classification. Deep neural networks can achieve this because they consist of millions of parameters that you train on large training datasets. For instance, the BERT […]
Read MoreBuilding a trash sorter with AWS DeepLens
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 custom data. Image classification is a […]
Read MoreBuilding an AI-powered Battlesnake with reinforcement learning on Amazon SageMaker
Battlesnake is an AI competition based on the traditional snake game in which multiple AI-powered snakes compete to be the last snake surviving. Battlesnake attracts a community of developers at all levels. Hundreds of snakes compete and rise up in the ranks in the online Battlesnake global arena. Battlesnake also hosts several offline events that […]
Read MoreCreating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker
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 how to make the runtime inference […]
Read MoreTraining batch reinforcement learning policies with Amazon SageMaker RL
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 (RL) models using Amazon SageMaker RL. […]
Read MoreUsing DeepChem with Amazon SageMaker for virtual screening
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 computer time and constant improvement of […]
Read MoreSimplify Machine Learning Inference on Kubernetes with Amazon SageMaker Operators
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 then run the endpoint, which requires […]
Read MoreAutomating model retraining and deployment using the AWS Step Functions Data Science SDK for Amazon SageMaker
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 create Step Functions-based machine learning workflows […]
Read MoreLowering total cost of ownership for machine learning and increasing productivity with Amazon SageMaker
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 data science teams. The Total Cost […]
Read MoreFlagging suspicious healthcare claims with Amazon SageMaker
The National Health Care Anti-Fraud Association (NHCAA) estimates that healthcare fraud costs the nation approximately $68 billion annually—3% of the nation’s $2.26 trillion in healthcare spending. This is a conservative estimate; other estimates range as high as 10% of annual healthcare expenditure, or $230 billion. Healthcare fraud inevitably results in higher premiums and out-of-pocket expenses […]
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