AWS Machine Learning Blog

Tag: Amazon Sagemaker

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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Power contextual bandits using continual learning with Amazon SageMaker RL

Amazon SageMaker is a modular, fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Training models is quick and easy using a set of built-in high-performance algorithms, pre-built deep learning frameworks, or using your own framework. To help select your machine learning (ML) algorithm, […]

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Speed up training on Amazon SageMaker using Amazon FSx for Lustre and Amazon EFS file systems

Amazon SageMaker provides a fully managed service for data science and machine learning workflows. One of the most important capabilities of Amazon SageMaker is its ability to run fully managed training jobs to train machine learning models. Now, you can speed up your training job runs by training machine learning models from data stored in Amazon FSx […]

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Modernizing wound care with Spectral MD, powered by Amazon SageMaker

Spectral MD, Inc. is a clinical research stage medical device company that describes itself as “breaking the barriers of light to see deep inside the body.” Recently designated by the FDA as a “Breakthrough Device,” Spectral MD provides an impressive solution to wound care using cutting edge multispectral imaging and deep learning technologies. This Dallas-based […]

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