|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/smart-analytics-machine-learning-ai-gcp
课程评论：没有评论
课程名称：在GCP上进行智能分析、机器学习和AI 课程概述：将机器学习纳入数据管道可以增强企业从数据中提取洞察的能力。本课程介绍了在Google Cloud上根据所需的定制化程度将机器学习纳入数据管道的几种方法。对于几乎不需要定制的情况，本课程涵盖了AutoML。对于更量身定制的机器学习能力，课程介绍了Notebooks和BigQuery机器学习（BigQuery ML）。此外，本课程还讲解了如何使用Kubeflow使机器学习解决方案可投入生产。学习者将通过QwikLabs获得在Google Cloud上构建机器学习模型的实践经验。 课程大纲： 1. **导言** 描述：介绍课程和议程。 2. **分析与AI入门** 描述：讨论Google Cloud上的机器学习选项。 3. **用于非结构化数据的预构建ML模型API** 描述：侧重于如何在非结构化数据上使用预构建的机器学习API。 4. **使用Notebooks进行大数据分析** 描述：讲解如何使用Notebooks。 5. **生产级机器学习管道** 描述：涵盖如何构建自定义机器学习模型，并介绍Vertex AI和AI Hub。 6. **在BigQuery ML中使用SQL构建自定义模型** 描述：讨论BigQuery ML。 7. **使用AutoML构建自定义模型** 描述：关于如何通过AutoML构建自定义模型的讲解。 8. **总结** 描述：回顾本课程涵盖的话题。
Name:Introduction
Description:In this module, we introduce the course and agenda
Name:Introduction to Analytics and AI
Description:This modules talks about ML options on Google Cloud
Name:Prebuilt ML model APIs for Unstructured Data
Description:This module focuses on using pre-built ML APIs on your unstructured data
Name:Big Data Analytics with Notebooks
Description:This module covers how to use Notebooks
Name:Production ML Pipelines
Description:This module covers building custom ML models and introduces Vertex AI and AI Hub
Name:Custom Model building with SQL in BigQuery ML
Description:This module covers BigQuery ML
Name:Custom Model Building with AutoML
Description:Custom model building with AutoML
Name:Summary
Description:This module recaps the topics covered in the course
Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.
