Category: SageMaker

Interpreting 3D seismic data correctly helps identify geological features that may hold or trap oil and gas deposits. Amazon SageMaker and Apache MXNet on AWS can automate horizon picking using deep learning techniques. In this post, I use these services to build and train a custom deep-learning model for the interpretation of geological features on […]

Developers are constantly training and re-training machine learning (ML) models so they can continuously improve model predictions. Depending on the dataset size, model training jobs can take anywhere from a few minutes to multiple hours or days. ML development can be a complex, expensive, and iterative process. Being compute intensive, keeping compute costs low for […]

Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant inspection grades and scores from AWS […]

TensorFlow is an open-source machine learning (ML) library widely used to develop heavy-weight deep neural networks (DNNs) that require distributed training using multiple GPUs across multiple hosts. Amazon SageMaker is a managed service that simplifies the ML workflow, starting with labeling data using active learning, hyperparameter tuning, distributed training of models, monitoring of training progression, […]

AWS is excited to introduce Amazon SageMaker Operators for Kubernetes, a new capability that makes it easier for developers and data scientists using Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. Customers can install these Amazon SageMaker Operators on their Kubernetes cluster to create Amazon SageMaker jobs natively using the […]

Businesses are increasingly developing per-user machine learning (ML) models instead of cohort or segment-based models. They train anywhere from hundreds to hundreds of thousands of custom models based on individual user data. For example, a music streaming service trains custom models based on each listener’s music history to personalize music recommendations. A taxi service trains […]

Machine learning (ML) is routinely used in every sector to make predictions. But beyond simple predictions, making decisions is more complicated because non-optimal short-term decisions are sometimes preferred or even necessary to enable long-term, strategic goals. Optimizing policies to make sequential decisions toward a long-term objective can be learned using a family of ML models […]

This is a guest post from Matt Fielder and Jordan Rosenblum at iHeartRadio. In their own words, “iHeartRadio is a streaming audio service that reaches tens of millions of users every month and registers many tens of thousands more every day.” Personalization is an important part of the user experience, and we aspire to give […]

Machine learning (ML) lets enterprises unlock the true potential of their data, automate decisions, and transform their business processes to deliver exponential value to their customers. To help you take advantage of ML, Amazon SageMaker provides the ability to build, train, and deploy ML models quickly. Until recently, if you used Amazon SageMaker, you could […]

AWS DeepRacer, launched at re:Invent 2018, helps developers get hands on with reinforcement learning (RL).  Since then, thousands of people have developed and raced their models at 21 AWS DeepRacer League events at AWS Summits across the world, and virtually via the AWS DeepRacer console. Beyond the summits there have been several events at AWS […]