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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ml-pipelines-google-cloud
课程评论：没有评论
课程名称：谷歌云上的机器学习管道（ML Pipelines on Google Cloud） 课程概述：本课程将由谷歌云的机器学习工程师和培训师授课，他们在机器学习管道的最前沿开发方面有着丰富的经验。课程的前几个模块主要围绕TensorFlow Extended（或称TFX）展开，这是一种基于TensorFlow的机器学习生产平台，旨在管理机器学习管道和元数据。学员将学习管道组件以及如何使用TFX进行管道协调。此外，课程还将介绍如何通过持续集成（CI）和持续部署（CD）自动化管道，以及如何管理机器学习元数据。 随后，课程将转向讨论如何在多个机器学习框架中（如TensorFlow、PyTorch、Scikit-learn和XGBoost）自动化和重用机器学习管道。学员还会学习如何使用谷歌云的另一个工具Cloud Composer来协调持续训练管道。课程最后将介绍如何利用MLflow管理整个机器学习生命周期。 课程难度为高级，为了获得最佳学习效果，建议学员具备以下前提条件： - 具备良好的机器学习基础，并拥有创建/部署机器学习管道的经验 - 完成谷歌云平台上TensorFlow机器学习专业化课（至少完成其中几门课程） - 完成MLOps基础课程 课程大纲包括： 1. 欢迎来到谷歌云上的机器学习管道 2. TFX管道介绍 3. 使用TFX的管道协调 4. TFX管道的自定义组件与CI/CD 5. TFX与机器学习元数据 6. 多SDK、KubeFlow与AI平台管道的持续训练 7. 使用Cloud Composer进行持续训练 8. 使用MLflow管理机器学习管道 9. 课程总结 请注意，注册本课程即表示您同意Qwiklabs的服务条款，详情请访问：https://qwiklabs.com/terms_of_service
Name:Welcome to ML Pipelines on Google Cloud
Description:This module introduces the course and shares the course outline
Name:Introduction to TFX Pipelines
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Name:Pipeline orchestration with TFX
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Name:Custom components and CI/CD for TFX pipelines
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Name:ML Metadata with TFX
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Name:Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
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Name:Continuous Training with Cloud Composer
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Name:ML Pipelines with MLflow
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Name:Summary
Description:
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. Please take note that this is an advanced level course and to get the most out of this course, ideally you have the following prerequisites: You have a good ML background and have been creating/deploying ML pipelines You have completed the courses in the ML with Tensorflow on GCP specialization (or at least a few courses) You have completed the MLOps Fundamentals course. >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
