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在 Google Cloud 控制台中运用您的技能

Michael Otmar Kaiser

成为会员时间：2020

探索生成式 AI - Vertex AI Earned May 9, 2024 EDT
生成式 AI 简介 Earned May 3, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Apr 24, 2024 EDT
Machine Learning Operations (MLOps): Getting Started Earned Apr 24, 2024 EDT
Computer Vision Fundamentals with Google Cloud Earned Apr 23, 2024 EDT
Production Machine Learning Systems Earned Apr 16, 2024 EDT
Machine Learning in the Enterprise Earned Apr 14, 2024 EDT
在 Vertex AI 上构建和部署机器学习解决方案 Earned Apr 13, 2024 EDT
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Apr 11, 2024 EDT
在 Google Cloud 上使用 TensorFlow 进行图片分类 Earned Apr 8, 2024 EDT
Feature Engineering Earned Mar 27, 2024 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud Earned Mar 24, 2024 EDT
面向机器学习的 BigQuery Earned Mar 18, 2024 EDT
在 Google Cloud 上为机器学习 API 准备数据 Earned Mar 17, 2024 EDT
Launching into Machine Learning Earned Mar 4, 2024 EST
Google Cloud 上的 AI 和机器学习简介 Earned Feb 26, 2024 EST

探索生成式 AI - Vertex AI 课程汇集了多组实验， 指导用户在 Google Cloud 平台上运用生成式 AI。参与实验，您将了解 如何使用 Vertex AI PaLM API 系列模型，包括 text-bison、chat-bison 和 textembedding-gecko。您还将了解提示设计、最佳实践， 以及如何使用生成式 AI 进行构思、文本分类、文本提取、文本 总结等任务。您还将学习如何通过 Vertex AI 自定义训练对基础模型进行调优， 并将模型部署到 Vertex AI 端点。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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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 will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.

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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 Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

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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 custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models. The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data. The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models. Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

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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. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

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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 best approach for data preprocessing. The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.

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完成在 Vertex AI 上构建和部署机器学习解决方案课程，赢取中级技能徽章。 在此课程中，您将了解如何使用 Google Cloud 的 Vertex AI Platform、AutoML 以及自定义训练服务来 训练、评估、调优、解释和部署机器学习模型。 此技能徽章课程的目标受众是专业的数据科学家和机器学习 工程师。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成此技能徽章课程 和作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。

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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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完成“在 Google Cloud 上使用 TensorFlow 进行图片分类”课程，赢取中级技能徽章 。在此课程中，您将学习如何使用 TensorFlow 和 Vertex AI 来创建和训练机器学习模型。您将主要使用 Vertex AI Workbench 上用户管理的 笔记本。

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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 engineering using BigQuery ML, Keras, and TensorFlow.

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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。

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完成入门级技能徽章课程在 Google Cloud 上为机器学习 API 准备数据，展示以下技能： 使用 Dataprep by Trifacta 清理数据、在 Dataflow 中运行数据流水线、在 Dataproc 中创建集群和运行 Apache Spark 作业，以及调用机器学习 API，包括 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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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. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.

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本课程介绍 Google Cloud 的 AI 和机器学习 (ML) 能力，重点讲解如何开发生成式和预测式 AI 项目。本课程将探讨“数据到 AI”全生命周期中的多种技术、产品和工具，并通过互动练习帮助数据科学家、AI 开发者和机器学习工程师提升专业能力。

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