AWS Machine Learning Blog

Category: SageMaker

Exploring data warehouse tables with machine learning and Amazon SageMaker notebooks

Are you a data scientist with data warehouse tables that you’d like to explore in your machine learning (ML) environment? If so, read on. In this post, I show you how to perform exploratory analysis on large datasets stored in your data warehouse and cataloged in your AWS Glue Data Catalog from your Amazon SageMaker […]

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Build end-to-end machine learning workflows with Amazon SageMaker and Apache Airflow

Machine learning (ML) workflows orchestrate and automate sequences of ML tasks by enabling data collection and transformation. This is followed by training, testing, and evaluating a ML model to achieve an outcome. For example, you might want to perform a query in Amazon Athena or aggregate and prepare data in AWS Glue before you train […]

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Build a custom data labeling workflow with Amazon SageMaker Ground Truth

Good machine learning models are built with large volumes of high-quality training data. But creating this kind of training data is expensive, complicated, and time-consuming. To help a model learn how to make the right decisions, you typically need a human to manually label the training data. Amazon SageMaker Ground Truth provides labeling workflows for […]

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Amazon SageMaker Object2Vec adds new features that support automatic negative sampling and speed up training

Today, we introduce four new features of Amazon SageMaker Object2Vec: negative sampling, sparse gradient update, weight-sharing, and comparator operator customization. Amazon SageMaker Object2Vec is a general-purpose neural embedding algorithm. If you’re unfamiliar with Object2Vec, see the blog post Introduction to Amazon SageMaker Object2Vec, which provides a high-level overview of the algorithm with links to four notebook […]

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End document drudgery with Alkymi’s AWS-powered automated data entry and document insights

Even in today’s highly digital workplace, documents are often manually processed in many enterprise workflows, including workflows in financial services.  Alkymi, founded by a team from Bloomberg and x.ai, enlists automation to streamline this laborious and error-prone work. Using deep learning models hosted on Amazon SageMaker, Alkymi identifies patterns and relationships in unstructured data and […]

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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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Use the wisdom of crowds with Amazon SageMaker Ground Truth to annotate data more accurately

Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for machine learning (ML). To get your data labeled, you can use your own workers, a choice of vendor-managed workforces that specialize in data labeling, or a public workforce powered by Amazon Mechanical Turk. The public workforce is large and economical but as […]

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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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