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Lex Xai

成为会员时间：2022

钻石联赛

26215 积分
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
TensorFlow on Google Cloud徽章 TensorFlow 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

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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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.

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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.

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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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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 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.

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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 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。

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Complete the introductory Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: writing gcloud commands and using Cloud Shell, creating and deploying virtual machines in Compute Engine, and configuring network and HTTP load balancers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

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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。

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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.

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“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。

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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。

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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。

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