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.

Building a Machine Learning Pipeline from Scratch.png

docker_setup.tar.gz

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omegaconf_example

dataset_model_examples

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pipeline_autompg_regression

reporting

pipeline_packaging

inference

unit_testing

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experiment_tracking

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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

Supporting a New Model

Introduction

Getting Started

Structuring the ML Pipeline

Directed Acyclic Graphs (DAGs)

The ML Library

Create Your First Data Pipeline with a Dashboard

The Pipeline Core

Extending the Pipeline

Build a News ETL Data Pipeline Using Python and SQLite

Testing

Deployment

Other Considerations

Wrapping Up

Appendix

Final Assessment

Supporting a New Model

Supporting a new model