加入 登录

Lex Xai

成为会员时间：2022

黄金联赛

42155 积分
Engineer Data for Predictive Modeling with BigQuery ML徽章 Engineer Data for Predictive Modeling with BigQuery ML Earned Dec 4, 2024 EST
Create ML Models with BigQuery ML徽章 Create ML Models with BigQuery ML Earned Nov 25, 2024 EST
Working with Notebooks in Vertex AI徽章 Working with Notebooks in Vertex AI Earned Nov 10, 2024 EST
Prepare Data for ML APIs on Google Cloud徽章 Prepare Data for ML APIs on Google Cloud Earned Nov 7, 2024 EST
Responsible AI for Developers: Interpretability & Transparency - 简体中文徽章 Responsible AI for Developers: Interpretability & Transparency - 简体中文 Earned Nov 6, 2024 EST
Responsible AI for Developers: Fairness & Bias - 简体中文徽章 Responsible AI for Developers: Fairness & Bias - 简体中文 Earned Nov 5, 2024 EST
Professional Machine Learning Engineer Study Guide徽章 Professional Machine Learning Engineer Study Guide Earned Nov 3, 2024 EST
Create Generative AI Apps on Google Cloud徽章 Create Generative AI Apps on Google Cloud Earned Nov 3, 2024 EST
Vector Search and Embeddings - 简体中文徽章 Vector Search and Embeddings - 简体中文 Earned Nov 3, 2024 EST
Build and Deploy Machine Learning Solutions on Vertex AI徽章 Build and Deploy Machine Learning Solutions on Vertex AI Earned Oct 29, 2024 EDT
ML Pipelines on Google Cloud徽章 ML Pipelines on Google Cloud Earned Oct 27, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation徽章 Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation Earned Oct 19, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI - 简体中文徽章 Machine Learning Operations (MLOps) for Generative AI - 简体中文 Earned Oct 11, 2024 EDT
Introduction to Large Language Models - 简体中文徽章 Introduction to Large Language Models - 简体中文 Earned Oct 11, 2024 EDT
Introduction to Generative AI - 简体中文徽章 Introduction to Generative AI - 简体中文 Earned Oct 10, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features徽章 Machine Learning Operations (MLOps) with Vertex AI: Manage Features Earned Oct 10, 2024 EDT
Machine Learning Operations (MLOps): Getting Started徽章 Machine Learning Operations (MLOps): Getting Started Earned Apr 7, 2024 EDT
Recommendation Systems on Google Cloud徽章 Recommendation Systems on Google Cloud Earned Apr 3, 2024 EDT
Natural Language Processing on Google Cloud徽章 Natural Language Processing on Google Cloud Earned Mar 26, 2024 EDT
Computer Vision Fundamentals with Google Cloud徽章 Computer Vision Fundamentals with Google Cloud Earned Mar 25, 2024 EDT
Production Machine Learning Systems徽章 Production Machine Learning Systems Earned Mar 23, 2024 EDT
Machine Learning in the Enterprise徽章 Machine Learning in the Enterprise Earned Mar 20, 2024 EDT
Feature Engineering徽章 Feature Engineering Earned Mar 9, 2024 EST
Build, Train and Deploy ML Models with Keras on Google Cloud徽章 Build, Train and Deploy ML Models with Keras on Google Cloud Earned Mar 5, 2024 EST
Launching into Machine Learning徽章 Launching into Machine Learning Earned Feb 26, 2024 EST
Introduction to AI and Machine Learning on Google Cloud - 简体中文徽章 Introduction to AI and Machine Learning on Google Cloud - 简体中文 Earned Feb 18, 2024 EST
Implement Load Balancing on Compute Engine徽章 Implement Load Balancing on Compute Engine Earned Jul 6, 2023 EDT
Reliable Google Cloud Infrastructure: Design and Process - 简体中文徽章 Reliable Google Cloud Infrastructure: Design and Process - 简体中文 Earned Jul 6, 2023 EDT
Developing a Google SRE Culture徽章 Developing a Google SRE Culture Earned Jul 2, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure - 简体中文徽章 Google Cloud Fundamentals: Core Infrastructure - 简体中文 Earned Jun 1, 2023 EDT
基准：基础架构徽章 基准：基础架构 Earned May 13, 2023 EDT
Google Cloud Essentials徽章 Google Cloud Essentials Earned Feb 8, 2023 EST

完成中级技能徽章课程利用 BigQuery ML 处理预测模型的工程师数据， 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； 利用 BigQuery ML 构建机器学习模型； 以及利用 Cloud Composer 跨多个位置复制数据。 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。

了解详情

完成中级技能徽章课程Create ML Models with BigQuery ML，展示您在以下方面的技能： 使用 BigQuery ML 构建和评估机器学习模型，以执行数据预测。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您在 Google Cloud 产品与服务方面的熟练度；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，即可获得技能徽章， 并在您的社交圈中秀一秀自己的能力。

了解详情

This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

了解详情

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。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可您在 Google Cloud 产品与服务方面的熟练度； 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。完成此技能徽章课程和作为最终评估的实验室挑战赛， 获得技能徽章，在您的人际圈中炫出自己的技能。

了解详情

本课程介绍了 AI 可解释性和透明度的相关概念，探讨了 AI 透明度对于开发者和工程师的重要性。同时探索了有助于在数据和 AI 模型中实现可解释性和透明度的实用方法及工具。

了解详情

本课程介绍了 Responsible AI 的概念和 AI 原则，还介绍了在 AI/机器学习实践中识别公平性与偏见以及减少偏见的实用技巧，同时探索了使用 Google Cloud 产品和开源工具来实施 Responsible AI 最佳实践的实用方法和工具。

了解详情

This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.

了解详情

Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud? In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.

了解详情

本课程介绍 Vertex AI Vector Search，并说明如何使用它来通过嵌入大语言模型 (LLM) API 构建搜索应用。本课程包括以下内容：讲解矢量搜索和文本嵌入概念的讲座、关于如何在 Vertex AI 上构建矢量搜索的实际演示，以及一项实操实验。

了解详情

完成使用 Vertex AI 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。

了解详情

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.

了解详情

This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.

了解详情

本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。

了解详情

这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

了解详情

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。

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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.

了解详情

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

了解详情

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.

了解详情

本课程介绍 Google Cloud 中的 AI 和机器学习 (ML) 服务，这些服务可构建预测式和生成式 AI 项目。本课程探讨从数据到 AI 的整个生命周期中可用的技术、产品和工具，包括 AI 基础、开发和解决方案。通过引人入胜的学习体验和实操练习，本课程可帮助数据科学家、AI 开发者和机器学习工程师提升技能和知识水平。

了解详情

完成入门级在 Compute Engine 上实现负载均衡技能徽章课程，展示自己在以下方面的技能： 编写 gcloud 命令和使用 Cloud Shell，在 Compute Engine 中创建和部署虚拟机， 以及配置网络和 HTTP 负载均衡器。 技能徽章是由 Google Cloud 颁发的专属数字徽章， 旨在认可您在 Google Cloud 产品与服务方面的熟练度； 该课程会检验您在交互式实操环境中运用所学知识的 能力。完成此技能徽章课程和作为最终评估的实验室挑战赛， 即可获得技能徽章，并在您的圈子中秀一秀。

了解详情

本课程指导学员运用久经考验的设计模式在 Google Cloud 上构建高度可靠且高效的解决方案。它是“Google Compute Engine 架构设计”或“Google Kubernetes Engine 架构设计”课程的延续，并假定您有使用其中任何一门课程所涵盖技术的实践经验。通过一系列演示、设计活动和动手实验，学员可以了解如何定义及平衡业务要求和技术要求，以便设计可靠性和可用性高、安全且经济实惠的 Google Cloud 部署。

了解详情

In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

了解详情

“Google Cloud 基础知识：核心基础架构”介绍在使用 Google Cloud 时会遇到的重要概念和术语。本课程通过视频和实操实验来介绍并比较 Google Cloud 的多种计算和存储服务，并提供重要的资源和政策管理工具。

了解详情

如果您是新手云开发人员，希望在GCP Essentials之外寻求动手实践，那么此任务适合您。通过深入研究Cloud Storage和其他关键应用程序服务（如Stackdriver和Cloud Functions）的实验室，您将获得实践经验。通过执行此任务，您将开发适用于任何GCP计划的宝贵技能。 1分钟的视频向您介绍这些实验室的关键概念。

了解详情

在此入门级挑战任务中，您可以使用 Google Cloud Platform 的基本工具和服务，开展真枪实弹的操作实训。“GCP 基本功能”是我们为 Google Cloud 学员推荐的第一项挑战任务。云知识储备微乎其微甚至零基础？不用担心！这项挑战任务会为您提供真枪实弹的实操经验，助您快速上手 GCP 项目。无论是要编写 Cloud Shell 命令还是部署您的第一台虚拟机，亦或是通过负载平衡机制或在 Kubernetes Engine 上运行应用，都可以通过“GCP 基本功能”了解该平台的基本功能之精要。点此观看 1 分钟视频，了解每个实验涉及的主要概念。

了解详情