This course bridges the gap between raw data and production-ready AI systems. In 2026, the value of a machine learning model is defined by the reliability of the data pipelines that feed it. This program transforms you into an MLOps-ready engineer capable of building automated, scalable, and observable data architectures.
Data Engineering Essentials
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Data Engineering Essentials
包含在 中
您将学到什么
Build scalable data pipelines using Pandas Polars and Apache Spark for diverse dataset sizes
Architect real time streaming solutions with Apache Kafka and feature stores for live ML inference
Automate complex ML workflows using Airflow and Prefect to ensure reliable continuous training
您将获得的技能
要了解的详细信息
可分享的证书
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最近已更新！
March 2026
作业
4 项作业
授课语言：英语（English）
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
本课程是 Hands-On MLOps Fundamentals for ML Engineers 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 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4个模块
Explore the foundational shift from traditional software development to data-centric machine learning operations. You will compare DevOps and MLOps workflows while mastering the core pillars of CI, CD, CT, and CM. This section establishes the architectural blueprint for building reliable and automated machine learning systems.
涵盖的内容
10个视频3篇阅读材料1个作业
Master the essential techniques for collecting and preparing high-quality data for machine learning models. You will implement robust ETL processes and explore the strategic role of Data Lakes in modern ML stacks. Hands-on labs with Pandas and Polars will provide practical experience in transforming raw datasets into clean features.
涵盖的内容
7个视频2篇阅读材料1个作业
Scale your engineering capabilities to handle massive datasets and real-time information flows. This module introduces distributed computing with Apache Spark and Dask alongside high-velocity streaming via Apache Kafka. You will also evaluate the critical role of Feature Stores in maintaining consistency between training and serving.
涵盖的内容
7个视频1篇阅读材料1个作业
Connect individual data tasks into a seamless and automated production pipeline using Airflow and Prefect. You will learn to manage complex dependencies and schedule automated training triggers to ensure model performance over time. This section focuses on making your data workflows resilient through advanced monitoring and error handling.
涵盖的内容
4个视频2篇阅读材料1个作业
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状态：免费试用Duke University
状态：免费试用Duke University
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常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
更多问题
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