Is Data Engineering & Pipeline Reliability for Machine Learning suitable for beginners?

This course is intended for learners with some experience in programming and machine learning. It focuses on engineering practices used to build reliable data pipelines for ML systems.

What tools will I learn in Data Engineering & Pipeline Reliability for Machine Learning?

You'll work with tools and practices commonly used in ML engineering, including data pipeline orchestration frameworks, version control systems like Git, and reproducible environment management tools.

Why are reliable data pipelines important for machine learning?

Machine learning models rely on consistent, high-quality data. Reliable pipelines ensure that data transformations are reproducible, scalable, and maintain performance as systems evolve.

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Specialization?

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.

Is financial aid available?

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.

Data Engineering & Pipeline Reliability for Machine Learning

Coursera PlusMonthly 3 个月课程4 折优惠 ，让你轻松掌握闪耀技能。立即节省

Data Engineering & Pipeline Reliability for Machine Learning

本课程是 Machine Learning Made Easy for Software Engineers 专项课程的一部分

位教师：Professionals from the Industry

包含在中

了解更多

10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

10个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Transform and validate data for machine learning using encoding, cleansing, and data quality techniques
Design and orchestrate ML data pipelines that ensure reliability, freshness, and pipeline performance
Manage reproducible ML development using version control and environment management tools

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Machine Learning Made Easy for Software 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10个模块

This course teaches you how to transform real-world datasets into reliable analytical assets through practical, reproducible data-cleaning techniques. You’ll learn how to evaluate categorical features and select optimal encoding strategies, measure and document data quality, and apply effective approaches to handle missing values. Using Python and pandas, you'll practice assessing cardinality, implementing target encoding, validating completeness with Great Expectations, and building transparent transformation lineage. You’ll also clean messy fields such as ages, salary outliers, and dates to ensure consistent model-ready outputs. Designed for analysts, data engineers, and ML practitioners, this course equips you with the job-ready skills needed to prepare high-quality datasets that support trustworthy insights and predictive modeling.

单元详情

You will analyze categorical features to determine the optimal encoding strategy based on cardinality and model fit considerations.

涵盖的内容

2个视频2篇阅读材料1个作业

You will evaluate data quality metrics and document data transformation lineage to ensure transparency and reliability.

涵盖的内容

1个视频1篇阅读材料1个作业

You will apply techniques to impute, flag, and validate missing or null values to produce consistent, model-ready datasets.

涵盖的内容

1个视频1篇阅读材料2个作业

You will apply ETL and ELT pipelines to ingest data from various sources into a feature store using structured transformation workflows.

涵盖的内容

2个视频1篇阅读材料1个作业

You will analyze upstream schema changes and implement safeguards to maintain data pipeline resilience and downstream compatibility.

涵盖的内容

2个视频1篇阅读材料

You will evaluate data freshness, lag, and pipeline success rates against service level agreements to assess operational reliability.

涵盖的内容

1个视频1篇阅读材料3个作业

1个视频总计4分钟

From Pipeline Runs to SLAs4分钟

1篇阅读材料总计6分钟

Seeing the Whole Pipeline: From Ingestion to SLAs 6分钟

3个作业总计75分钟

Hands-On Activity: Interpreting Pipeline Metrics and Detecting SLA Breaches 15分钟
Hands-On Activity: End-to-End ML of a Pipeline Reliability Lab40分钟
Graded Quiz: Evaluating ML Pipeline Design and Reliability20分钟

You will apply version control branching strategies to manage code, experiments, and project artifacts effectively.

涵盖的内容

3个视频1篇阅读材料2个作业

3个视频总计24分钟

Welcome & Course Introduction Video3分钟
How Git Branching Supports ML Development7分钟
Creating a Feature Branch and Managing Artifacts14分钟

1篇阅读材料总计6分钟

Comparing Git workflows: What you should know6分钟

2个作业总计25分钟

Hands-On Activity: Create a Feature Branch and Push ML Artifacts20分钟
Practice Quiz: Branching Patterns, Commit Hygiene, Artifact Management 5分钟

You will apply virtual environment tools to configure reproducible project environments with stable dependencies.

涵盖的内容

2个视频1篇阅读材料1个非评分实验室

You will analyze resource utilization across CPU, GPU, and memory usage to optimize compute costs during experimentation.

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计23分钟

Understanding Compute Cost in ML Development8分钟
Spotting Resource Bottlenecks and Moving Jobs to Cheaper Compute15分钟

1篇阅读材料总计6分钟

VS Code Remote Development for ML Workflows 6分钟

2个作业总计40分钟

Hands-On Activity: Analyze Resource Metrics and Recommend Cost Optimization Actions20分钟
Graded Quiz: ML Development Optimization 20分钟

In this project, you will design and implement a production-style machine learning data pipeline for a financial services risk modeling scenario. The raw dataset contains missing values, inconsistent categorical entries, potential outliers, and simulated schema drift. Your task is to transform this dataset into a validated, model-ready feature store. You will clean and preprocess structured tabular data, select encoding strategies based on feature cardinality, implement data validation using Great Expectations, detect schema changes between pipeline runs, generate SLA metrics to assess reliability, and save processed features in parquet format. Beyond the core pipeline, you will also apply professional development practices that are standard in production ML teams: setting up a virtual environment for reproducibility, using version control branching strategies to manage your work, and analyzing resource utilization to understand compute costs. Your final deliverable is a modular Python script and a structured written engineering explanation that demonstrates your ability to design reliable, production-aligned ML data infrastructure.