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Building a Machine Learning Pipeline from Scratch

Learn how to build a machine learning pipeline entirely from scratch, using software engineering best practices.

Beginner

42 Lessons

14h

Certificate of Completion

Learn how to build a machine learning pipeline entirely from scratch, using software engineering best practices.

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Explanations

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Explanations

This course includes

1 Assessment

30 Playgrounds

7 Quizzes

Course Overview

What You'll Learn

Course Content

Recommendations

Course Overview

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 ...Show More

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

An understanding of what constitutes as a machine learning training pipeline

Course Content

Introduction

1 Lessons

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

Course Goals and Structure

Getting Started

3 Lessons

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.

Managing Dependencies

Quiz: Structuring the ML Pipeline

Directed Acyclic Graphs (DAGs)

3 Lessons

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

What Is a DAG?

Topological Sorting

Quiz: DAGs

The ML Library

8 Lessons

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

Object-Oriented Programming

Configuration Files: OmegaConf

Abstract Base Classes

The Pipeline Core

7 Lessons

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

Structuring the Pipeline Core

Parsing Command-Line Arguments: Argparse

Experiment Tracking

Logging

The Complete Pipeline

Docstrings

Quiz: The Pipeline Core

Extending the Pipeline

2 Lessons

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

Supporting a New Dataset

Supporting a New Model

Testing

4 Lessons

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

Introduction to Unit Testing

Pytest

Introduction to System Testing

Quiz: Testing

Deployment

2 Lessons

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

Packaging the ML Library

Inference

10.

Other Considerations

4 Lessons

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

Data Quality and Model Performance Monitoring

Reproducibility

Off-the-Shelf Solutions

Quiz: Observability and Reproducibility

11.

Wrapping Up

1 Lessons

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

Conclusion

12.

Appendix

1 Lessons

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

Useful Resources

Final Assessment

Assessment

Course Author

Jayanth Chennamangalam

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