菜单

本页面中的信息并不完全以您的首选语言展示,我们正在完善其他语言版本。想要以您的首选语言了解相关信息,可以点击这里下载PDF。

技术

机器学习下的持续交付

May 2020
试验?

利用机器学习使业务应用和服务智能化,并不仅仅是训练模型并为其提供服务。 它需要实现一整套端到端、持续可重复的模型训练、测试、部署、监控和运维周期。机器学习下的持续交付 (CD4ML) 是一种可靠的端到端开发、部署和监控机器学习模型的技术。支撑CD4ML的基础技术栈包括数据访问和探索工具、工件(例如数据、模型和代码)的版本控制、持续交付流水线、用于各种部署和实验的自动化环境设置、模型性能评估和跟踪,以及模型运作的可观测性。公司可以根据现有的技术栈选择自己的工具集。CD4ML强调自动化和避免手工交接。CD4ML是我们开发机器学习模型的默认方法。

Nov 2019
试验?

随着基于ML的应用程序的日益普及以及构建它们所涉及的技术复杂性,我们的团队严重依赖于机器学习的持续交付(CD4ML),以安全快速且可持续的方式交付此类应用程序。CD4ML是将CD原理和实践引入ML应用程序的学科。它消除了从训练模型到部署生产环境的长周期。在构建和部署模型的端到端过程中,CD4ML消除了不同团队、数据工程师、数据科学家和ML工程师之间的手动传递。使用CD4ML,我们的团队成功地实现了基于ML的应用程序所有组件的自动化版本管理,测试和部署,包括数据,模型和代码。

Apr 2019
试验?

Continuous delivery for machine learning (CD4ML) models apply continuous delivery practices to developing machine learning models so that they are always ready for production. This technique addresses two main problems of traditional machine learning model development: long cycle time between training models and deploying them to production, which often includes manually converting the model to production-ready code; and using production models that had been trained with stale data.

A continuous delivery pipeline of a machine learning model has two triggers: (1) changes to the structure of the model and (2) changes to the training and test data sets. For this to work we need to both version the data sets and the model's source code. The pipeline often includes steps such as testing the model against the test data set, applying automatic conversion of the model (if necessary) with tools such as H2O, and deploying the model to production to deliver value.