This short course helps you build and validate ML-ready data pipelines with confidence. You’ll start by learning how to design ETL workflows that ingest, clean, and partition large datasets using tools like Airflow and Spark. You’ll see how real teams manage click-stream logs, handle nulls, and prepare partitioned training data at scale. Next, you’ll evaluate data quality, governance, and lineage so your pipelines remain trustworthy and reproducible. You’ll work with practical techniques like schema drift checks, expectations suites, and audit-ready lineage records. Through short videos, applied readings, hands-on practice, and a final graded assessment, you’ll walk away knowing how to engineer reliable pipelines and validate them for production use.
Engineer, Validate, and Govern ML Data
Engineer, Validate, and Govern ML Data
本课程是多个项目的一部分。
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要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有1个模块
This short course helps you build and validate ML-ready data pipelines with confidence. You’ll start by learning how to design ETL workflows that ingest, clean, and partition large datasets using tools like Airflow and Spark. You’ll see how real teams manage click-stream logs, handle nulls, and prepare partitioned training data at scale. Next, you’ll evaluate data quality, governance, and lineage so your pipelines remain trustworthy and reproducible. You’ll work with practical techniques like schema drift checks, expectations suites, and audit-ready lineage records. Through short videos, applied readings, hands-on practice, and a final graded assessment, you’ll walk away knowing how to engineer reliable pipelines and validate them for production use.
涵盖的内容
6个视频3篇阅读材料3个作业1个非评分实验室
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