AWS Machine Learning Blog

Category: SageMaker

Control root access to Amazon SageMaker notebook instances

Amazon SageMaker recently introduced the ability to enable and disable root access for notebook users. Before I give you a preview of how you can implement this new feature using the AWS Management Console and Amazon SageMaker API actions, I’ll explain why controlling root access for users is helpful. Amazon SageMaker provides fully managed notebook […]

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Become a certified machine learning developer with the new AWS Certified Machine Learning – Specialty certification

Back in November 2018 we announced on this blog that the same machine learning (ML) courses used to train engineers at Amazon are now available to all developers through AWS. Today, we’re letting you know that there is a way to enhance and validate your ability to build, train, tune, and deploy machine learning models […]

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Bring your own hyperparameter optimization algorithm on Amazon SageMaker

In this blog post, we’ll discuss how to implement custom, state-of-the-art hyperparameter optimization (HPO) algorithms to tune models on Amazon SageMaker. Amazon SageMaker includes a built-in HPO algorithm, but provides the flexibility to use your own HPO algorithm. We’ll provide you with a framework to incorporate an HPO algorithm that you choose. However, before we […]

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Use two additional data labeling services for your Amazon SageMaker Ground Truth labeling jobs

We’re excited to announce the availability of two more data labeling services that you can use for your Amazon SageMaker Ground Truth labeling jobs: Data Labeling Services by iMerit’s US-based workforce Data Labeling Services by Startek, Inc. These new listings on the AWS Marketplace supplement the existing iMerit India-based workforce listing to provide you a […]

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Preprocess input data before making predictions using Amazon SageMaker inference pipelines and Scikit-learn

Amazon SageMaker enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale. You can deploy trained ML models for real-time or batch predictions on unseen data, a process known as inference. However, in most cases, the raw input data must be preprocessed and can’t be used directly for […]

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Identifying bird species on the edge using the Amazon SageMaker built-in Object Detection algorithm and AWS DeepLens

Custom object detection has become an important enabler for a wide range of industries and use cases—such as finding tumors in MRIs, identifying diseased crops, and monitoring railway platforms. In this blog post, we build a bird identifier based on an annotated public dataset. This type of model could be used in a number of […]

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Using TensorFlow eager execution with Amazon SageMaker script mode

In this blog post, I’ll discuss how to use Amazon SageMaker script mode to train models with TensorFlow’s eager execution mode. Eager execution is the future of TensorFlow; although it is available now as an option in recent versions of TensorFlow 1.x, it will become the default mode of TensorFlow 2. I’ll provide a brief […]

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DXC Technology automates triage of support tickets using AWS machine learning

DXC Technology is a global IT service leader providing end-to-end services on Digital Transformation to businesses and governments. They also provide service management to their clients on-premises and in the cloud.  The incident tickets raised as part of the process need to be resolved quickly to meet their service level agreements (SLA).  DXC has  goals […]

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Deploy trained Keras or TensorFlow models using Amazon SageMaker

Amazon SageMaker makes it easier for any developer or data scientist to build, train, and deploy machine learning (ML) models. While it’s designed to alleviate the undifferentiated heavy lifting from the full life cycle of ML models, Amazon SageMaker’s capabilities can also be used independently of one another; that is, models trained in Amazon SageMaker […]

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Thoughts on Recent Research Paper and Associated Article on Amazon Rekognition

A research paper and associated article published yesterday made claims about the accuracy of Amazon Rekognition. We welcome feedback, and indeed get feedback from folks all the time, but this research paper and article are misleading and draw false conclusions. This blog post shares details which we hope will help clarify several ‎misperceptions and inaccuracies. […]

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