作为首个实操实验，此实验将引导您访问 Google Cloud 控制台并使用 Google Cloud 的以下基本功能：项目、资源、IAM 用户、角色、权限和 API。

本课程介绍 Google Cloud 中的人工智能 (AI) 和机器学习 (ML) 服务，这些服务支持数据到 AI 的生命周期（从 AI 基础、AI 开发到 AI 解决方案）。我们将探索一系列技术、产品和工具；利用这些工具，可基于不同用户（包括数据科学家、AI 开发者和机器学习工程师）的目标构建机器学习模型、机器学习流水线和生成式 AI 项目。

The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code....

This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature...

This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the...

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,...

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building...

This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine...

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners...

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...

Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and...

Earn a skill badge by completing the Build and Deploy Machine Learning Solutions with Vertex AI course, where you will learn how to use Google Cloud's unified Vertex AI platform and its AutoML and custom training services to train, evaluate,...