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所在平台: Udemy |
课程主页: https://www.udemy.com/course/cloudera-cdp-machine-learning-engineer-exam-guide-cdp-6001/
课程评论：没有评论
课程名称：Cloudera CDP 机器学习工程师考试指南 CDP-6001 概述： 该课程旨在帮助学员充分发挥 Cloudera 数据平台在机器学习方面的潜力，是为准备 CDP-6001 考试而设计的全面、场景导向的备考课程。通过四份完整的模拟试卷和 45 个精心设计的问题，学员将掌握在企业环境中主导机器学习项目所需的技能。 课程内容： 1. **CML 基础**：了解 Cloudera 机器学习工作区，学习创建和组织项目，配置和管理实验，使用“加速器”启动模型管道。深入了解运行时管理（包括 GPU 支持），并掌握数据可视化工具以进行探索性分析和利益相关者报告。 2. **Spark for ML 工程师**：提升处理大数据集的能力，熟练掌握使用 Spark DataFrames 读取和写入多种文件格式（CSV、Parquet、ORC），以及应用窗口函数进行时间序列和聚合，构建可扩展的数据转换管道，为 ML 特征工程奠定基础。 3. **使用 Spark MLlib 进行模型训练**：更深入地设计端到端模型管道。学习选择合适的机器学习算法，链式连接变换器和估算器，利用网格搜索和管道优化超参数，严格地拟合和评估模型（回归、分类、聚类）。提高模型的可重复性和模块化。 4. **模型部署与 MLOps**：弥合笔记本与生产环境之间的差距。通过 CML 使用 REST API 部署模型，集成 MLflow 实现版本控制和生命周期管理，并配置自动扩展以确保负载下的性能。同时实现监控和指标，主动检测漂移或性能下降，确保生产级服务水平协议（SLA）。 课程优势： - **以考试为重点且实用**：每份试卷都反映真实案例研究，而不仅仅是抽象问题。 - **实践技能**：学员不仅能通过考试，还将掌握管理机器学习工作流程所需的技能，从开发到生产。 - **现代机器学习管道**：涵盖 GPU 加速、MLflow 和 API 部署等工具，这些都是当前顶尖机器学习团队使用的。 - **结构化学习路径**：从基础的机器学习工作区开始，逐步过渡到数据工程，再到模型创建，甚至到全生产部署。 通过本课程的学习，学员将自信地应对 CDP-6001 认证考试，并成为能够在企业环境中设计和操作端到端机器学习解决方案的机器学习工程师。
CDP Machine Learning Engineer (CDP‑6001) PrepUnlock the full potential of Cloudera's Data Platform for machine learning excellence with this comprehensive, scenario-based preparation course tailored for the CDP‑6001 exam. Through four full-length practice papers and 45 meticulously designed questions, you'll build the exact skills required to lead ML initiatives in enterprise environments.Paper 1 - CML FundamentalsStart with the Cloudera Machine Learning workspace: create and organize projects, configure and manage experiments, and use "accelerators" to jumpstart model pipelines. Dive into runtime management (including GPU support), and master data visualization tools for exploratory analysis and stakeholder reporting.Paper 2 - Spark for ML EngineersAdvance by processing large datasets with Spark DataFrames. Gain proficiency in reading/writing multiple file formats (CSV, Parquet, ORC), applying window functions for time-series and aggregations, and building scalable data transformation pipelines-laying the groundwork for ML feature engineering.Paper 3 - Model Training with Spark MLlibDive deeper as you design end-to-end model pipelines. Learn to select the right ML algorithms, chain transformers and estimators, optimize hyperparameters with grid search and pipelines, and rigorously fit and evaluate models (regression, classification, clustering). Enhance model reproducibility and modularity.Paper 4 - Model Deployment & MLOpsBridge the gap between notebooks and production. Deploy models via REST APIs using CML, integrate MLflow for versioning and lifecycle management, and configure autoscaling to ensure performance under load. You'll also implement monitoring and metrics to proactively detect drift or degradation, and uphold production-grade SLAs.Why This Course Stands OutExam-Focused Yet Practical: Each paper mirrors real-world case studies, not just abstract questions.Hands-On Skills: Beyond passing the exam, you'll acquire skills to manage ML workflows, from development to production.Built for Modern ML Pipelines: Covers GPU acceleration, MLflow, API deployments-tools used by top ML teams today.Structured Learning Path: Begin with foundational ML workspaces, move to data engineering, then model creation-even to full production deployment.By the end of this course, you'll be fully prepared to tackle the CDP‑6001 certification with confidence-and will emerge as an ML Engineer capable of architecting and operating end-to-end machine learning solutions in enterprise environments.
