AWS Machine Learning Blog

Category: SageMaker

Udacity’s Machine Learning Nanodegree now includes Amazon SageMaker 

During the past few years, the demand for machine learning specialists and engineers has soared. These two roles now rank among the top emerging jobs on LinkedIn. More recently, machine learning is being adopted by a wide range of industries, from medical diagnostic companies to finance firms and more. Udacity created the Intro to Machine […]

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Analyze content with Amazon Comprehend and Amazon SageMaker notebooks

In today’s connected world, it’s important for companies to monitor social media channels to protect their brand and customer relationships. Companies try to learn about their customers, products, and services through social media, emails, and other communications. Machine learning (ML) models can help address some of these needs. However, the process to build and train […]

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Extending Amazon SageMaker factorization machines algorithm to predict top x recommendations

Amazon SageMaker gives you the flexibility that you need to address sophisticated business problems with your machine learning workloads. Built-in algorithms help you get started quickly.  In this blog post we’ll outline how you can extend the built-in factorization machines algorithm to predict top x recommendations. This approach is ideal when you want to generate […]

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Amazon SageMaker automatic model tuning now supports random search and hyperparameter scaling

We are excited to introduce two highly requested features to automatic model tuning in Amazon SageMaker: random search and hyperparameter scaling. This post describes these features, explains when and how to enable them, and shows how they can improve your search for hyperparameters that perform well. If you are in a hurry, you’ll be happy […]

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Build a serverless anomaly detection tool using Java and the Amazon SageMaker Random Cut Forest algorithm

One of the problems that business owners commonly face is detecting when something unusual is happening in their business. Detecting unusual user activity or changes in daily traffic patterns are just some of the challenges. With an ever-increasing amount of data and metrics, detecting anomalies with the help of machine learning is a great way […]

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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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