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Building a Machine Learning Pipeline from Scratch

Name: Building a Machine Learning Pipeline from Scratch
Price: 59 USD
Rating: 4.35 (43 reviews)
Author: Jayanth Chennamangalam

Learn about ML pipeline development, delve into best practices, discover advanced Python concepts, and explore testing methodologies to elevate your software engineering skills and career prospects.

4.3

42 lessons

2 projects

14h

Created by Jayanth Chennamangalam

Building a Machine Learning Pipeline from Scratch

What's included

42 lessons across 12 modules
7 quizzes with instant feedback
2 projects in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

An understanding of what constitutes as a machine learning training pipeline
Hands-on experience building a machine learning pipeline in Python
Familiarity with advanced Python concepts, such as abstract base classes and mixins
An understanding of software engineering best practices, including code style, documentation, and logging
A working knowledge of unit testing in Python

Learning Roadmap

42 Lessons7 Quizzes1 Assessment

Introduction

Get familiar with building and deploying a machine learning pipeline from scratch.

Course Goals and Structure

Getting Started

Look at how traditional engineering practices enhance ML pipeline stability and collaboration.

Motivation: Why Jupyter Notebooks Aren’t Enough

The ML Training Pipeline

Quiz: Getting Started

Structuring the ML Pipeline

6 Lessons

Break apart the ML pipeline structure, directory organization, code style, and dependency management.

Directed Acyclic Graphs (DAGs)

3 Lessons

Break down the steps to construct and sort DAGs for machine learning tasks.

The ML Library

8 Lessons

Unveil object-oriented implementation, configuration management, dataset and model handling, and report generation in ML pipelines.

Create Your First Data Pipeline with a Dashboard

Project

Premium

The Pipeline Core

7 Lessons

Follow the process of structuring machine learning pipelines, including argument parsing, logging, and tracking experiments.

Extending the Pipeline

2 Lessons

Build on extending machine learning pipelines to support new datasets and models effectively.

Build a News ETL Data Pipeline Using Python and SQLite

Project

Premium

Testing

4 Lessons

Step through unit testing, Pytest for code coverage, and comprehensive system testing.

Deployment

2 Lessons

Get started with packaging and deploying machine learning models for consistent predictions.

10.

Other Considerations

4 Lessons

Explore considerations for data quality monitoring, reproducibility, and leveraging off-the-shelf ML solutions.

11.

Wrapping Up

Grasp the fundamentals of building, managing, and deploying a machine learning pipeline.

Conclusion

12.

Appendix

Take a closer look at essential resources for machine learning pipelines, libraries, and frameworks.

Useful Resources

Final Assessment

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

Machine learning (ML) has matured into a mainstream development activity, and data scientists are expected to be able to write production-grade training pipelines. This course will provide you with a foundation in ML pipeline development guided by best practices in software engineering. You’ll start by learning about code organization, style, and conceptual ideas, such as topological sorting of directed acyclic graphs. You’ll dive into the hands-on development of an ML pipeline. You’ll learn some advanced Python concepts and useful libraries. Finally, you’ll learn about testing methodologies and how to package your code into a distributable library. By the end of this course, you’ll understand what makes a great ML pipeline work, better appreciate software engineering processes, improve your Python knowledge, and learn how to build ML training pipelines. This knowledge will make you more attractive to potential employers and improve your chances of thriving as a new data scientist.

ABOUT THE AUTHOR

Jayanth Chennamangalam

I am a full-stack data scientist based in Vancouver, British Columbia, Canada. Previously, I was an astronomer and a software engineer.

Learn more about Jayanth

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