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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/gcp-production-ml-systems
课程评论：没有评论
课程名称：生产机器学习系统 课程概述：该课程涵盖了如何实现各种类型的生产机器学习系统，包括静态、动态和持续训练；静态和动态推理；批量处理和在线处理。学员将深入了解TensorFlow的抽象层次、分布式训练的各种选项，以及如何使用自定义估计器编写分布式训练模型。 课程大纲： 1. **高级机器学习在谷歌云上的介绍** 该模块预览了课程中涵盖的主题，并介绍如何使用Qwiklabs完成每个实验室的练习，借助谷歌云进行操作。 2. **生产机器学习系统架构** 本模块探讨了生产机器学习系统需要满足的其他要求。学员将回顾如何进行高层设计决策，以确保训练和模型服务的需要能实现最佳性能。 3. **设计可适应的机器学习系统** 在此模块中，学员将学习如何识别模型对数据的依赖关系，做出具有成本效益的工程决策，了解何时回滚模型到早期版本，调试观察到的模型行为的原因，以及实施对某种依赖类型免疫的管道。 4. **设计高性能的机器学习系统** 本模块让学员识别机器学习模型的性能考虑因素。因为机器学习模型并不完全相同，有些模型侧重于提高输入/输出性能，而另一些则注重提高计算速度。 5. **构建混合机器学习系统** 学员将理解可用的工具和系统，并学习在何时利用混合机器学习模型进行最佳实践。 6. **总结** 本模块回顾了课程中学习的内容，加深对所学知识的理解。 这个课程适合希望深入了解生产环境中机器学习系统实施的学员，无论是在模型训练、推理还是性能优化方面。
Name:Introduction to Advanced Machine Learning on Google Cloud
Description:This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
Name:Architecting Production ML Systems
Description:This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
Name:Designing Adaptable ML Systems
Description:In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
Name:Designing High-Performance ML Systems
Description:In this module, you identify performance considerations for machine learning models. Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
Name:Building Hybrid ML Systems
Description:Understand the tools and systems available and when to leverage hybrid machine learning models.
Name:Summary
Description:This module reviews what you learned in this course.
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
