What is the translation of " AMAZON SAGEMAKER " in Chinese?

• amazon sagemaker
• 亚马逊sagemaker

Examples of using Amazon sagemaker in English and their translations into Chinese

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

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

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

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

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

DigitalGlobe is all in on AWS and uses Amazon SageMaker to handle machine learning at scale.
DigitalGlobe将一切托付于AWS并使用AmazonSageMaker来大规模处理机器学习。

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

To make the training even faster and easier, Amazon SageMaker can automatically tune your model to achieve the highest possible accuracy.
为了使训练过程甚至更快更轻松，AmazonSageMaker可以自动调整您的模型以达到最高的精度。

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

Then you will learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.”.
然后你将学习使用亚马逊的机器学习来解决更简单的一类机器学习问题，而亚马逊的SageMaker来解决更复杂的问题。

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

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

Kumar Venkateswar is a Product Manager in the AWS ML Platforms team, which includes Amazon SageMaker, Amazon Machine Learning, and the AWS Deep Learning AMIs.
KumarVenkateswar是AWSML平台团队的产品经理，AWSML平台包括AmazonSageMaker、AmazonMachineLearning和AWSDeepLearningAMI。

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

NASA uses a machine-learning tool called Amazon SageMaker to train an anomaly detection model using the built-in AWS Random Cut Forest algorithm.
NASA使用一种称为AmazonSageMaker的机器学习工具来使用内置的AWSRandomCutForest算法训练异常检测模型。

Amazon SageMaker also comes pre-configured to run TensorFlow, Apache MXNet, and Chainer in Docker containers.
此外，AmazonSageMaker还经过预配置，能在Docker容器中运行TensorFlow、ApacheMXNet和Chainer。

The scalability of Amazon SageMaker, and its ability to integrate with native AWS services, adds enormous value for us.
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。

Amazon SageMaker protects buyers data by employing security measures such as static scans, network isolation, and runtime monitoring.
AmazonSageMaker通过采用静态扫描，网络隔离和运行时监控等安全措施来保护买方数据。

Chris used Amazon SageMaker and Polly to implement ASLens(you can watch the video, learn more and read the code).
ASLens是Chris使用AmazonSageMaker和Polly实现的（您可以观看视频来了解详情，也可以阅读该程序的代码）。

Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
AmazonSageMaker包含一些可一起或单独使用的模块以构建、训练和部署您的机器学习模型。

Amazon SageMaker will significantly reduce the complexity of machine learning, enabling us to create a better experience for our customers, fast.”.
AmazonSageMaker将显著降低机器学习的复杂性，帮助我们快速为客户提供更好的体验。

By leveraging Amazon SageMaker and AWS's machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time.
通过利用AmazonSageMaker和AWS的机器学习服务，我们能够为粉丝提供有洞察力的实时赛事见解和预测。

Amazon SageMaker also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results.
AmazonSageMaker还包含内置的A/B测试功能，以帮助您测试模型并试验不同的版本以获得最佳效果。

Amazon SageMaker Ground Truth helps customers build highly accurate training datasets quickly using machine learning and reduce data labeling costs by up to 70%.
AmazonSageMakerGroundTruth可以建立高精确的训练数据集，利用机器学习减少高达70%的数据标记成本。

Amazon SageMaker RL builds on top of Amazon SageMaker, adding pre-packaged RL toolkits and making it easy to integrate any simulation environment.
AmazonSageMakerRL构建于AmazonSageMaker之上，添加了预先打包的RL工具包，可以轻松集成任何模拟环境。

Amazon SageMaker simplifies machine learning, helping our development teams to build models for predictions that create new connections that otherwise might have never been possible.”.
AmazonSageMaker简化了机器学习，帮助我们的开发团队建立预测模型，从而创建前所未有的新的连接。

And AWS built Amazon SageMaker, a fully managed machine learning service that empowers everyday developers and scientists to use machine learning- without any previous experience.
AWS构建了AmazonSageMaker，这是一种完全托管的机器学习服务，可让日常开发人员和科学家无需任何前置经验即可运用机器学习。

Amazon SageMaker now supports the k-Nearest-Neighbor(kNN) and Object Detection algorithms to address additional identification, classification, and regression use cases in machine learning.
AmazonSageMaker现已支持使用K-近邻算法(kNN)和目标检测算法来处理机器学习中的更多识别、分类和回归用例。

Today Amazon SageMaker has open sourced the MXNet and Tensorflow deep learning containers that power the MXNet and Tensorflow estimators in the SageMaker SDK.
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。