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

Building a Machine Learning Pipeline from Scratch.png

docker_setup.tar.gz

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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.

Building a Machine Learning Pipeline from Scratch

Notebooks can’t be run programmatically.

**(Select all that apply.)** What is *true* regarding Notebooks? 

(Select all that apply.) What is true regarding Notebooks?

**(True or False)** If you build an ML training pipeline the traditional way (as a command-line script), you’ll need to build one pipeline per ML problem.

(True or False) If you build an ML training pipeline the traditional way (as a command-line script), you’ll need to build one pipeline per ML problem.

**(True or False)** An ML training pipeline can be represented as a DAG.

(True or False) An ML training pipeline can be represented as a DAG.

**(True or False)** EDA is the first step in any ML training pipeline.

(True or False) EDA is the first step in any ML training pipeline.

Test your understanding of the lessons in this chapter.

Introduction

Getting Started

Structuring the ML Pipeline

Directed Acyclic Graphs (DAGs)

The ML Library

The Pipeline Core

Extending the Pipeline

Testing

Deployment

Other Considerations

Wrapping Up

Appendix

Final Assessment

Quiz: Getting Started