AWS Machine Learning Blog

Tag: Amazon SageMaker

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 […]

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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 […]

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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, […]

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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 […]

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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 […]

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Developing a business strategy by combining machine learning with sensitivity analysis

Machine learning (ML) is routinely used by countless businesses to assist with decision making. In most cases, however, the predictions and business decisions made by ML systems still require the intuition of human users to make judgment calls. In this post, I show how to combine ML with sensitivity analysis to develop a data-driven business […]

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Optimizing portfolio value with Amazon SageMaker automatic model tuning

Financial institutions that extend credit face the dual tasks of evaluating the credit risk associated with each loan application and determining a threshold that defines the level of risk they are willing to take on. The evaluation of credit risk is a common application of machine learning (ML) classification models. The determination of a classification […]

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Building a deep neural net–based surrogate function for global optimization using PyTorch on Amazon SageMaker

Optimization is the process of finding the minimum (or maximum) of a function that depends on some inputs, called design variables. Customer X has the following problem: They are about to release a new car model to be designed for maximum fuel efficiency. In reality, thousands of parameters that represent tuning parameters relating to the […]

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Performing batch inference with TensorFlow Serving in Amazon SageMaker

After you’ve trained and exported a TensorFlow model, you can use Amazon SageMaker to perform inferences using your model. You can either: Deploy your model to an endpoint to obtain real-time inferences from your model. Use batch transform to obtain inferences on an entire dataset stored in Amazon S3. In the case of batch transform, […]

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