What is the translation of " MACHINE LEARNING PIPELINES " in Chinese?

• 机器学习管道

Examples of using Machine learning pipelines in English and their translations into Chinese

One of the use cases for using machine learning pipelines is text categorization.
机器学习流水线的用例之一就是文字分类。

In this section we tackle the broad problem of explainable machine learning pipelines.
在本节中，我们将解决可解释的机器学习pipeline的广泛问题。

Machine Learning pipelines and models can now be persisted across all languages supported by Spark.
机器学习管线和模型现已能够持久保存，Spark所支持的所有语言均支持这一特性。

This package can be used for developing and managing the machine learning pipelines.
这个包可以用于开发和管理机器学习流水线。

Machine Learning Pipelines: an easy-to-use API for complete machine learning workflows.
MachineLearningPipelines：针对完整的机器学习工作流，是一个易用的API。

What if there was an automated service that identifies the best machine learning pipelines for a given problem/data?
如果有一个自动服务可以识别给定问题/数据的最佳机器学习管道，该怎么办？?

Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions.
最后，他们构建机器学习流程，打造个性化的数据产品，以更好地了解他们的业务和客户，并做出更好的决策。

In particular, Spark ML is focused on using the rich, higher-level DataFrame API to create machine learning pipelines.
特别是，SparkML关注于使用丰富的、更高级的DataFrameAPI创建机器学习管道。

This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale.
该课程教授在PB级别的开发结构化和非结构化数据的可扩展的机器学习管道所需的基本原则。

This API allows building predictive models that include supervised and unsupervised machine learning tasks, as well as machine learning pipelines.
这个API允许构建预测模型，包括有监督和无监督的机器学习任务，以及机器学习管道。

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
它基本上是一个Python自动机器学习工具，使用遗传编程来优化机器学习工作流。

Apache Spark MLlib's DataFrame-based API provides a simple, yet flexible and elegant framework for creating end-to-end machine learning pipelines.
ApacheSparkMLlib's基于数据帧的API提供简单而又灵活的出色框架，用于创建端到端的机器学习管道。

Machine learning pipeline persistence: Users can now save and load machine learning pipelines and models across all programming languages supported by Spark.
机器学习管道持久化：用户现在可以利用Spark支持的所有编程语言保存和加载机器学习管道和模型。

Along the way, you will learn how to‘understand the task by understanding the data' and how to build fully functioning machine learning pipelines.
在这个过程中，你将会学习如何通过理解数据来理解任务，以及如何构建具有完整功能的机器学习管道。

Building and deploying large-scale machine learning pipelines: why we need primitives, pipeline synthesis tools, and most importantly, error analysis and verification.
开发和部署大规模机器学习管道》：BenRecht讲述为什么我们需要原语、管道同步工具，以及最重要的，对错误的分析和验证。

TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
TPOT是一种Python自动化机器学习工具，可以使用遗传编程优化机器学习管道.

Spark MLlib includes a framework for creating machine learning pipelines, allowing for easy implementation of feature extraction, selections, and transformations on any structured dataset.
SparkMLlib包含一个用于创建机器学习管道的框架，允许在任何结构化数据集上轻松实现特征提取、选择和转换。

Nabar's novel approach is to build a“meta” machine learning framework that automates the building of entire machine learning pipelines.
Nabar的新方法是建立一个“元（meta）”机器学习框架，然后自动化的构建整个机器学习流程。

We need to rethink all of our machine learning pipeline to make it more robust," says Aleksander Madry, a computer scientist at MIT.
我们需要重新考虑我们所有的机器学习管道，以使其更加强大，”麻省理工学院计算机科学家亚历山大·马德里说。

The input to the machine learning pipeline is a video stream from a normal webcam pointed out the window:.
机器学习流程的输入是来自一个伸出窗外的普通网络摄像头的视频流：.

The scientific process of training and evaluating the machine learning pipeline creates highly accurate predictions to help you win.
训练和评估机器学习管道这一科学流程可以做出高度准确的预测，从而帮您取得成功。

Another machine learning pipeline use case is the image classification as described in this article.
另一个机器学习流水线用例就是在这篇文章中描述的图像分类。

The following table shows the different steps involved in a machine learning pipeline process.
下图展示了在机器学习流水线处理中涉及到的不同步骤。

Exhibit a highly specialized understanding of the modern machine learning pipeline: data, models, algorithmic principles, and empirics;
展示了对现代机器学习管道的高度专业化的理解：数据，模型，算法原理和经验；.

It builds on the classical machine learning pipeline many data scientists are using: Python programs made with Numpy, pandas, and scikit-learn.
它基于许多数据科学家正在使用的经典机器学习管道构建：使用Numpy、panda和scikit-learn编写的Python程序。

A typical standard machine learning pipeline based on the Cross-industry standard process for a data mining industry standard process model is depicted below.
本文描述了一个典型的基于跨行业标准流程的标准机器学习管道，作为数据挖掘行业的标准过程模型。

Deliver Scalable Compute Infrastructures- Gartner points out“The second most time-intensive portion of the machine learning pipeline is usually the model engineering phase.”.
提供可扩展的计算基础设施--Gartner指出：“机器学习管道中的第二大时间密集型部分通常是模型工程设计阶段。

Feature selection, the process of finding and selecting the most useful features in a dataset, is a crucial step of the machine learning pipeline..
特征选择，即在数据集中查找和选择最有用的特征的过程，是机器学习的关键步骤。