AWS Machine Learning Blog

Category: Amazon SageMaker

Use Amazon SageMaker Data Wrangler in Amazon SageMaker Studio with a default lifecycle configuration

If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment […]

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Secure Amazon SageMaker Studio presigned URLs Part 1: Foundational infrastructure

You can access Amazon SageMaker Studio notebooks from the Amazon SageMaker console via AWS Identity and Access Management (IAM) authenticated federation from your identity provider (IdP), such as Okta. When a Studio user opens the notebook link, Studio validates the federated user’s IAM policy to authorize access, and generates and resolves the presigned URL for […]

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Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

In part 1 of this series, we demonstrated how to resolve an Amazon SageMaker Studio presigned URL from a corporate network using Amazon private VPC endpoints without traversing the internet. In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API […]

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Use a custom image to bring your own development environment to RStudio on Amazon SageMaker

RStudio on Amazon SageMaker is the industry’s first fully managed RStudio Workbench in cloud. You can quickly launch the familiar RStudio integrated development environment (IDE), and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. RStudio on […]

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Text classification for online conversations with machine learning on AWS

Online conversations are ubiquitous in modern life, spanning industries from video games to telecommunications. This has led to an exponential growth in the amount of online conversation data, which has helped in the development of state-of-the-art natural language processing (NLP) systems like chatbots and natural language generation (NLG) models. Over time, various NLP techniques for […]

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Hyperparameter optimization for fine-tuning pre-trained transformer models from Hugging Face

Large attention-based transformer models have obtained massive gains on natural language processing (NLP). However, training these gigantic networks from scratch requires a tremendous amount of data and compute. For smaller NLP datasets, a simple yet effective strategy is to use a pre-trained transformer, usually trained in an unsupervised fashion on very large datasets, and fine-tune […]

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New built-in Amazon SageMaker algorithms for tabular data modeling: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, […]

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Semantic segmentation data labeling and model training using Amazon SageMaker

In computer vision, semantic segmentation is the task of classifying every pixel in an image with a class from a known set of labels such that pixels with the same label share certain characteristics. It generates a segmentation mask of the input images. For example, the following images show a segmentation mask of the cat […]

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Deep demand forecasting with Amazon SageMaker

Every business needs the ability to predict the future accurately in order to make better decisions and give the company a competitive advantage. With historical data, businesses can understand trends, make predictions of what might happen and when, and incorporate that information into their future plans, from product demand to inventory planning and staffing. If […]

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Inspect your data labels with a visual, no code tool to create high-quality training datasets with Amazon SageMaker Ground Truth Plus

Launched at AWS re:Invent 2021, Amazon SageMaker Ground Truth Plus helps you create high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. All you do is share data along with labeling requirements, and Ground Truth Plus sets up and manages your data labeling workflow […]

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