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

4 days left! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Data Engineering & Pipeline Reliability for Machine Learning

This course is part of Machine Learning Made Easy for Software Engineers Specialization

Instructor: Professionals from the Industry

Included with

Learn more

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

10 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Machine Learning Made Easy for Software Engineers Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 10 modules in this course

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.

Module details

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

What's included

2 videos2 readings1 assignment

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

What's included

1 video1 reading1 assignment

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

What's included

1 video1 reading2 assignments

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

What's included

2 videos1 reading1 assignment

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

What's included

2 videos1 reading

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

What's included

1 video1 reading3 assignments

1 videoTotal 4 minutes

From Pipeline Runs to SLAs4 minutes

1 readingTotal 6 minutes

Seeing the Whole Pipeline: From Ingestion to SLAs 6 minutes

3 assignmentsTotal 75 minutes

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

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

What's included

3 videos1 reading2 assignments

3 videosTotal 24 minutes

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

1 readingTotal 6 minutes

Comparing Git workflows: What you should know6 minutes

2 assignmentsTotal 25 minutes

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

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

What's included

2 videos1 reading1 ungraded lab

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

What's included

2 videos1 reading2 assignments

2 videosTotal 23 minutes

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

1 readingTotal 6 minutes

VS Code Remote Development for ML Workflows 6 minutes

2 assignmentsTotal 40 minutes

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

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.