This is the code repository for Machine Learning Infrastructure and Best Practices for Software Engineers, published by Packt.

Take your machine learning software from a prototype to a fully fledged software system

Machine learning is an important driver of innovation in software products. This book will help you take your machine learning prototype to the next level and scale it up using concepts such as data provisioning, processing, and quality control.

This book covers the following exciting features:

• Identify what the machine learning software best suits your needs
• Work with scalable machine learning pipelines
• Scale up pipelines from prototypes to fully fledged software
• Choose suitable data sources and processing methods for your product
• Differentiate raw data from complex processing, noting their advantages
• Track and mitigate important ethical risks in machine learning software
• Work with testing and validation for machine learning systems

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter01.

The code will look like the following:

def fibRec(n):
 if n < 2:
     return n
 else:
     return fibRec(n-1) + fibRec(n-2)

Following is what you need for this book: If you’re a machine learning engineer, this book will help you design more robust software, and understand which scaling-up challenges you need to address and why. Software engineers will benefit from best practices that will make your products robust, reliable, and innovative. Decision makers will also find lots of useful information in this book, including guidance on what to look for in a well-designed machine learning software product.

With the following software and hardware list you can run all code files present in the book (Chapter 1-17).

• Practical Machine Learning on Databricks [Packt] [Amazon]

• Machine Learning for Imbalanced Data [Packt] [Amazon]

Miroslaw Staron is a Professor at the Department of Computer Science and Engineering, University of Gothenburg, Sweden. He has published extensively on software metrics, model-driven software development and empirical software engineering, and has worked together with Ericsson, Volvo, and other telecom companies and car manufacturers. His research interests include advanced machine learning in software engineering as well as in telecommunications and critical care.