Machine Learning at Scale - Mandarin Chinese

在本课程中，您将深入了解 Apache Spark 的架构及其在 Databricks 中应用于机器学习工作负载的理论与实践知识。您将学习何时使用 Spark 进行数据准备、模型训练和部署，同时获得使用 Spark ML 以及 Pandas APIs on Spark 的实践经验。本课程将向您介绍高级概念，例如超参数调优以及使用 Spark 扩展 Optuna。本课程将使用助理课程中介绍的功能和概念，例如 MLflow 和 Unity Catalog，以实现全面的模型打包与治理。

注意: 对于 SCORM 讲授文件，请确保在完成内容后关闭 SCORM 窗口。请勿点击“Next Lesson”按钮，否则可能导致 SCORM 模块无法被标记为已完成。

Skill Level

Professional

Duration

Prerequisites

本课程内容面向具备以下技能、知识和能力的学员：

• 熟悉 Databricks Data Intelligence Platform 及基本工作区运营（创建集群、在笔记本中运行代码、使用基本笔记本操作、从 Git 导入 Repos）

• 具备中级 Python 编程经验，包括数据处理库（Pandas、NumPy）和机器学习框架（Scikit-Learn）

• 掌握 Apache Spark 和 PySpark 基础知识，包括 DataFrames、转换及分布式数据处理操作

• 理解机器学习概念，包括模型训练、评估、超参数调优及部署工作流

• 具备中级 Delta Lake 运营经验（创建表、执行更新、优化文件、时间旅行功能）

• 基本熟悉 MLflow 的实验追踪、模型记录及 Model Registry 运营

• 理解分布式计算概念（集群架构、并行化、可扩展性考量）

• 掌握基本 SQL 知识，用于在 Spark 环境中进行数据查询与处理

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Model Development at Scale

In this course, you will learn to design and implement machine learning workflows that scale from a single machine to distributed compute on Databricks. The curriculum is organized into three modules: foundations of model development (when and why to distribute, and how Spark executes work), scalable model development with Spark ML and MLflow, and distributed model training with Ray and PyTorch — closing with a framework for choosing the right tool for a given workload.

Through hands-on demos and labs, you will build distributed Spark ML pipelines using Transformers, Estimators, and the fit/transform paradigm; scale existing pandas code with the Pandas API on Spark; and track, compare, and govern experiments with MLflow 3 and Unity Catalog. You will then train models across a cluster with Ray Train and PyTorch (TorchDistributor and DeepSpeed), and learn to match each workload to the right framework based on data size, model size, hardware, and cost.

The course concludes with a capstone that takes a problem end to end — engineering features, training a model at scale, tracking it with MLflow, and registering it in Unity Catalog with a governance alias. (Production deployment, serving, and monitoring are covered in the separate Advanced MLOps course.)

By the end, you will be able to choose, build, and justify distributed, reproducible, and governed model-development workflows on Databricks.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

Free

Professional

Data Engineer

Get Started with Data Governance on Databricks - Mandarin Chinese

在本课程中，您将通过动手演示和综合实验室，浏览 Unity Catalog 并了解 Databricks 上的细粒度访问控制。您将学习表类型、目录和架构配置、基于组的访问管理以及访问控制迁移策略。本课程包含以下演示：应用基于行级安全和列掩码的细粒度访问控制、基于属性的访问控制、组合控制、迁移控制，以及一个用于全面治理实施的实验室。

Languages Available: English | 日本語 | 한국어 | Português BR | Français | Español | 普通话

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Onboarding