Machine Learning Engineering with Python
eBook - ePub

Machine Learning Engineering with Python

Andrew P. McMahon

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  1. 276 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Machine Learning Engineering with Python

Andrew P. McMahon

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About This Book

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments

Key Features

  • Explore hyperparameter optimization and model management tools
  • Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages
  • Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases

Book Description

Machine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services.Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems.By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.

What you will learn

  • Find out what an effective ML engineering process looks like
  • Uncover options for automating training and deployment and learn how to use them
  • Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions
  • Understand what aspects of software engineering you can bring to machine learning
  • Gain insights into adapting software engineering for machine learning using appropriate cloud technologies
  • Perform hyperparameter tuning in a relatively automated way

Who this book is for

This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.

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Information

Year
2021
ISBN
9781801077101

Section 1: What Is ML Engineering?

The objective of this section is to provide a discussion of what activities could be classed as ML engineering and how this constitutes an important element of using data to generate value in organizations. You will also be introduced to an example software development process that captures the key aspects required in any successful ML engineering project.
This section comprises the following chapters:
  • Chapter 1, Introduction to ML Engineering
  • Chapter 2, The Machine Learning Development Process

Chapter 1: Introduction to ML Engineering

Welcome to Machine Learning Engineering with Python, a book that aims to introduce you to the exciting world of making Machine Learning (ML) systems production-ready.
This book will take you through a series of chapters covering training systems, scaling up solutions, system design, model tracking, and a host of other topics, to prepare you for your own work in ML engineering or to work with others in this space. No book can be exhaustive on this topic, so this one will focus on concepts and examples that I think cover the foundational principles of this increasingly important discipline.
You will get a lot from this book even if you do not run the technical examples, or even if you try to apply the main points in other programming languages or with different tools. In covering the key principles, the aim is that you come away from this book feeling more confident in tackling your own ML engineering challenges, whatever your chosen toolset.
In this first chapter, you will learn about the different types of data role relevant to ML engineering and how to distinguish them; how to use this knowledge to build and work within appropriate teams; some of the key points to remember when building working ML products in the real world; how to start to isolate appropriate problems for engineered ML solutions; and how to create your own high-level ML system designs for a variety of typical business problems.
We will cover all of these aspects in the following sections:
  • Defining a taxonomy of data disciplines
  • Assembling your team
  • ML engineering in the real world
  • What does an ML solution look like?
  • High-level ML system design
Now that we have explained what we are going after in this first chapter, let's get started!

Technical requirements

Throughout the book, we will assume that Python 3 is installed and working. The following Python packages are used in this chapter:
  • Scikit-learn 0.23.2
  • NumPy
  • pandas
  • imblearn
  • Prophet 0.7.1

Defining a taxonomy of data disciplines

The explosion of data and the potential applications of that data over the past few years have led to a proliferation of job roles and responsibilities. The debate that once raged over how a data scientist was different from a statistician has now become extremely complex. I would argue, however, that it does not have to be so complicated. The activities that have to be undertaken to get value from data are pretty consistent, no matter what business vertical you are in, so it should be reasonable to expect that the skills and roles you need to perform these steps will also be relatively consistent. In this chapter, we will explore some of the main data disciplines that I think you will always need in any data project. As you can guess, given the name of this book, I will be particularly keen to explore the notion of ML engineering and how this fits into the mix.
Let's now look at some of the roles involved in using data in the modern landscape.

Data scientist

Since the Harvard Business Review declared that being a data scientist was The Sexiest Job of the 21st Century (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century), this title has become one of the most sought after, but also hyped, in the mix. A data scientist can cover an entire spectrum of duties, skills, and responsibilities depending on the business vertical, the organization, or even just personal preference. No matter how this role is defined, however, there are some key areas of focus that should always be part of the data scientist's job profile:
  • Analysis: A data scientist should be able to wrangle, mung, manipulate, and consolidate datasets before performing calculations on that data that help us to understand it. Analysis is a broad term, but it's clear that the end result is knowledge of your dataset that you didn't have before you started, no matter how basic or complex.
  • Modeling: The thing that gets everyone excited (potentially including you, dear reader) is the idea of modeling data. A data scientist usually has to be able to apply statistical, mathematical, and machine learning models to data in order to explain it or perform some sort of prediction.
  • Working with the customer or user: The data science role usually has some more business-directed elements so that the results of steps 1 and 2 can support decision making in the organization. This could be done by presenting the results of analysis in PowerPoints or Jupyter notebooks or even sending an email with a summary of the key results. It involves communication and business acumen in a way that goes beyond classic tech roles.

ML engineer

A newer kid on the block, and indeed the subject of this book, is the ML engineer. This role has risen to fill the perceived gap between the analysis and modeling of data science and the world of software products and robust systems engineering.
You can articulate the need for this type of role quite nicely by considering a classic voice assistant. In this case, a data scientist would usually focus on translating the business requirements into a working speech-to-text model, potentially a very complex neural network, and showing that it can perform the desired voice transcription task in principle. ML engineering is then all about how you take that speech-to-text model and build it into a product, service, or tool that can be used in production. Here, it may mean building some software to train, retrain, deploy, and track the performance of the model as more transcription data is accumulated, or user preferences are understood. It may also involve understanding how to interface with other systems and how to provide results from the model in the appropriate formats, for example, interacting with an online store.
Data scientists and ML engineers have a lot of overlapping skill sets and competencies, but have different areas of focus and strengths (more on this later), so they will usually be part of the same project team and may have either title, but it will be clear what hat they are wearing from what they do in that project.
Similar to the data scientist, we can define the key areas of focus for the ML engineer:
  • Translation: Taking models and research code in a variety of formats and translating this into slicker, more robust pieces of code. This could be done using OO programming, functional programming, a mix, or something else, but basically helps to take the Proof-Of-Concept work of the data scientist and turn it into something that is far closer to being trusted in a production environment.
  • Architecture: Deployments of any piece of software do not occur in a vacuum and will always involve lots of integrated parts. This is true of machine learning solutions as well. The ML engineer ...

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