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所在平台: Udemy |
课程主页: https://www.udemy.com/course/google-professional-machine-learning-engineer-test/
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课程名称：2025年Google专业机器学习工程师认证考试准备课程 课程概述： 本课程旨在帮助学员准备Google Cloud认证的专业机器学习工程师考试。课程提供经验证的问题与答案，提供2025年的练习测试资源。专业机器学习工程师通过使用Google Cloud技术及经过验证的模型与技术，构建、评估、投入生产和优化机器学习（ML）模型。该角色需要处理大型复杂数据集，并创建可重复、可复用的代码。同时，工程师在整个ML模型开发过程中必须关注负责任的人工智能与公平性，并与其他工作角色紧密合作，以确保基于ML的应用程序的长期成功。机器学习工程师需要具备强大的编程技能和对数据平台以及分布式数据处理工具的经验，并熟悉模型架构、数据及ML管道的创建、以及指标解读等内容。此外，工程师还需要了解MLOps的基础概念、应用开发、基础设施管理、数据工程和数据治理。通过训练、再训练、部署、调度、监控及改进模型，机器学习工程师设计并创建可扩展和高性能的解决方案。 专业机器学习工程师认证考试评估学员的能力，包括： - 架构低代码ML解决方案 - 团队内部及跨团队协作管理数据与模型 - 将原型规模化为ML模型 - 部署与扩展模型 - 自动化与编排ML管道 - 监控ML解决方案 该课程面向希望在机器学习和人工智能领域提升技能，准备相关认证考试的学习者。
Are you ready to prepare for the Google Cloud Certified Professional Machine Learning Engineer exam ?Get Verified Questions and Answers Practice tests 2025A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.The Professional Machine Learning Engineer exam assesses your ability to:Architect low-code ML solutionsCollaborate within and across teams to manage data and modelsScale prototypes into ML modelsServe and scale modelsAutomate and orchestrate ML pipelinesMonitor ML solutions
