11/15/2023 0 Comments Aws sagemaker clarify![]() With SageMaker Data Wrangler’s visualization templates, you can quickly preview and inspect that these transformations are completed as you intended by viewing them in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. ![]() SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleaning, exploration, and visualization from a single visual interface. ![]() Amazon SageMaker Data Wrangler: Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepares data for machine learning (ML) from weeks to minutes. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. Amazon SageMaker JumpStart: Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data. As part of the workflows, labelers have access to assistive labeling features such as automatic 3D cuboid snapping, removal of distortion in 2D images, and auto-segment tools to reduce the time required to label datasets. ![]() These workflows support a variety of use cases including 3D point clouds, video, images, and text. Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. Amazon SageMaker Ground Truth: Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality. SageMaker Autopilot will automatically explore different solutions to find the best model. With SageMaker Autopilot, you simply provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). Amazon SageMaker Autopilot: Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models, and helps you automatically build, train, and tune the best ML model based on your data. Accelerate innovation with purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, auto-ML, training, tuning, hosting, explainability, monitoring, and workflows. Amazon SageMaker Studio: Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.
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