AWS Machine Learning Blog

Category: SageMaker

Interpreting 3D seismic data automatically using Amazon 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 […]

Read More

Cinnamon AI saves 70% on ML model training costs with Amazon SageMaker Managed Spot Training

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 […]

Read More

Building machine learning workflows with AWS Data Exchange and Amazon SageMaker

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 […]

Read More

Running distributed TensorFlow training with Amazon SageMaker

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, […]

Read More

Introducing Amazon SageMaker Operators for Kubernetes

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 […]

Read More

Save on inference costs by using Amazon SageMaker multi-model endpoints

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 […]

Read More

Automating financial decision making with deep reinforcement learning

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 […]

Read More

Real-time music recommendations for new users with Amazon SageMaker

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 […]

Read More

Adding AI to your applications with ready-to-use models from AWS Marketplace

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 […]

Read More

Custom deep reinforcement learning and multi-track training for AWS DeepRacer with Amazon SageMaker RL Notebook

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 […]

Read More