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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-on-google-cloud-platform/
课程评论：没有评论
课程名称：在谷歌云（Vertex AI）上进行机器学习 - 实践操作！ 课程概述：这门课程针对希望理解云平台的数据科学家和人工智能从业者，无论是初学者还是高级从业者。课程开始时，提供了谷歌云平台（GCP）的概述，包括创建GCP账户和基本操作理解。在深入GCP的人工智能服务之前，课程还介绍了GCP的重要服务，包括计算、存储、数据库、身份和访问管理（IAM）以及分析服务，并展示了这些服务的关键组成部分。 课程的后三个部分专注于理解和使用GCP提供的AI服务。学员将学习如何利用AutoML为表格数据、图像和文本数据创建和部署模型，并通过API获取已部署模型的预测结果。在AI平台部分，学员将使用图形用户界面（GUI）和编程方式创建和部署模型，创建和提交任务并评估训练模型。同时，课程还介绍了使用Kubeflow创建流水线的过程。 在Vertex AI部分，学员将专注于使用AutoML进行模型创建以及自定义模型的训练和部署，还将学习自定义模型中的超参数优化步骤，并使用AutoML和自定义模型创建Kubeflow流水线。最后，课程还涵盖了特征存储的相关内容。 本课程适合希望将机器学习活动迁移至谷歌云平台的学员，为他们提供了实际操作机会和深入了解AI服务的能力。
Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP?If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.This course starts with providing an overview of the Google Cloud Platform, creating a GCP account, and providing a basic understanding of the platform. Before jumping into the AI services of GCP, this course introduces important services of GCP. Services include Compute, storage, database, IAM, and analytics, followed by a demo of one key component of these services. The last three sections of the course are dedicated to understanding and working on the AI services offered by GCP. You will work on model creation and deployment using AutoML for tabular, images, and text data. Getting predictions from the deployed model using APIs. In the AI platform section, you will work on model creation and deployment using AI Platform (both GUI and coding approach). Creation and submission of jobs and evaluation of the trained model. Pipeline creation using Kubeflow. And in the Vertex AI section, you will work on model creation using AutoML, custom model training, and deployment. Inclusion ofhyperparameter optimization step in the custom model. Kubeflow pipelines creation using AutoML & custom models. You will also work on the Feature store.
