Machine Learning Engineering with Python
Andrew P. McMahon
- 276 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Machine Learning Engineering with Python
Andrew P. McMahon
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.
]]>
Frequently asked questions
Information
Section 1: What Is ML Engineering?
- Chapter 1, Introduction to ML Engineering
- Chapter 2, The Machine Learning Development Process
Chapter 1: Introduction to ML Engineering
- 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
Technical requirements
- Scikit-learn 0.23.2
- NumPy
- pandas
- imblearn
- Prophet 0.7.1
Defining a taxonomy of data disciplines
Data scientist
- 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
- 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 ...