What is the translation of " SAGEMAKER " in English?

• sagemaker

Examples of using Sagemaker in Chinese and their translations into English

AmazonSageMaker用于构建、训练和部署机器学习模型。
Amazon SageMaker to build, train, and deploy ML models.

在演讲中，Jassy强调了SageMaker的灵活性。
In his presentation, Jassy emphasized SageMaker's flexibility.

AmazonSageMaker包括三个模块：构建、训练和部署。
Amazon SageMaker includes three modules: Build, Train, and Deploy.

快速、完全托管的训练：AmazonSageMaker使训练变得更加容易了。
Fast, fully managed training: Amazon SageMaker makes training easy.

SageMaker还具有灵活性，能够满足我们不同的生产要求。
Amazon SageMaker is also flexible for our different production requirements.

GBDX笔记本和亚马逊SageMaker系统地挖掘地理空间数据.
GBDX Notebooks and Amazon SageMaker for systematic mining of geospatial data.

AmazonSageMaker现在支持其内置TensorFlow容器的版本1.10。
Amazon SageMaker now supports version 1.9 in its pre-built TensorFlow containers.

DigitalGlobe如何使用AmazonSageMaker来大规模管理机器学习.
DigitalGlobe also uses Amazon SageMaker to handle machine learning at scale.

AmazonSageMaker和AWSDeepLens使机器学习惠及所有开发人员.
Amazon SageMaker and AWS DeepLens make machine learning accessible to all developers.

今天，成千上万的客户正在使用SageMaker在AWS上构建机器学习模型。
Today, thousands of customers are using SageMaker to build machine learning models on top of AWS.

AmazonSageMaker持续地快速迭代并代表客户发布新功能。
Amazon SageMaker continues to iterate quickly and release new features on behalf of customers.

今天，成千上万的客户正在使用Sagemaker在AWS之上构建机器学习模型。
Today, thousands of customers are using SageMaker to build machine learning models on top of AWS.

AmazonSageMaker消除了阻碍开发人员成功完成每个步骤的复杂性。
Amazon SageMaker removes the complexity that holds back developer success with each of these steps.

一旦有了自己的模型，Jassy说你可以从SageMaker运行它，或者在另一个服务上使用它。
Once you have your model, Jassy said you could run it from SageMaker or use it on another service.

AmazonSageMaker消除了通常会阻碍开发人员使用机器学习的所有障碍。
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

亚马逊已经在他们的AWS进行大量创新，比如其最近推出构建和部署ML模型的Sagemaker。
Amazon has presented large innovations in their AWS, such as their recent presentation of Sagemaker to build and deploy ML model.

AmazonSageMakerGroundTruth可以选择使用主动学习来自动标记输入数据。
Amazon SageMaker Ground Truth can optionally use active learning to automate the labeling of your input data.

他继续补充到，“自推出SageMaker以来的一年里，我们进行了快速迭代，为该服务发布了200多个主要新特性和功能。
Jassy continued,“In the year since we launched SageMaker, we have iterated quickly and have released over 200 major new features and capabilities for the service.

SageMaker消除了机器学习过程中每个步骤的繁重，复杂性和猜测-使AI民主化。
SageMaker removes the heavy lifting, complexity, and guesswork from each step of the machine learning process- democratizing AI.

在设计过程中，他使用的是名为SageMaker的亚马逊云服务，这是一款机器学习产品，专为那些对机器学习一无所知的应用程序开发人员设计。
He did it using an Amazon cloud service called SageMaker, a machine-learning product designed for app developers who know nothing about machine learning.

亚马逊SageMaker的可扩展性及其与本地AWS服务集成的能力，为我们带来了巨大的价值。
The scalability of Amazon SageMaker, and its ability to integrate with native AWS services, adds enormous value for us.

该芯片能够与TensorFlow和PyTorch等主要框架协同工作，并兼容亚马逊机器学习服务SageMaker和AWS的EC2实例类型。
Inferentia will work with major frameworks like TensorFlow and PyTorch and is compatible with EC2 instance types and Amazon's machine learning service SageMaker.

亚马逊的Sagemaker和Google的AutoML提供基于云的工具，来自动创建机器学习模型。
Amazon's Sagemaker and Google's AutoML provide cloud-based tools to automate the creation of machine learning models.

去年11月，在AWSre:Invent大会上，亚马逊为其客户推出了一个更全面的机器学习产品:SageMaker，一个成熟且超级易用的平台。
Last November at the AWS re: Invent conference, Amazon unveiled a more comprehensive machine-learning prosthetic for its customers: SageMaker, a sophisticated but super easy-to-use platform.

除了SageMakerStudio，AWS今日还宣布了SageMaker的诸多其它更新，现均已集成到Studio中。
In addition to Studio, AWS also today announced a number of other updates to SageMaker that are integrated into Studio.

SageMaker旨在为客户提供各种预建开发环境，让日常开发人员和科学家更容易建立自己的定制机器学习系统。
Called SageMaker, it's designed to make it easier for everyday developers and scientists to build their own custom machine learning systems.

从今天开始，SageMaker增加了对许多新实例类型、使用SDK的本地设置以及ApacheMXNet1.1.0和Tensorflow1.6.0的支持。
Starting today, SageMaker adds support for many new instance types, local testing with the SDK, and Apache MXNet 1.1.0 and Tensorflow 1.6.0.

SageMakerNeo最初于2018年11月在AWSRe:Invent上推出，旨在帮助开发人员针对硬件平台优化现有机器学习模型。
SageMaker Neo was first launched on AWS Re: Invent in November 2018 to help developers optimize existing machine learning models for hardware platforms.

亚马逊的SageMaker简化了构建、培训和部署机器学习流程的工作，在一个开放的市场上提供了100多种算法和模型。
Amazon's SageMaker simplifies the job of building, training and then deploying a machine learning process, offering more than 100 algorithms and models in an open marketplace.

今天，AmazonSageMaker构建了开源型MXNet和Tensorflow深度学习容器，助力SageMakerSDK中的MXNet和Tensorflow估计器。
Today Amazon SageMaker has open sourced the MXNet and Tensorflow deep learning containers that power the MXNet and Tensorflow estimators in the SageMaker SDK.