Book Image

The Machine Learning Solutions Architect Handbook

By : David Ping
Book Image

The Machine Learning Solutions Architect Handbook

By: David Ping

Overview of this book

When equipped with a highly scalable machine learning (ML) platform, organizations can quickly scale the delivery of ML products for faster business value realization. There is a huge demand for skilled ML solutions architects in different industries, and this handbook will help you master the design patterns, architectural considerations, and the latest technology insights you’ll need to become one. You’ll start by understanding ML fundamentals and how ML can be applied to solve real-world business problems. Once you've explored a few leading problem-solving ML algorithms, this book will help you tackle data management and get the most out of ML libraries such as TensorFlow and PyTorch. Using open source technology such as Kubernetes/Kubeflow to build a data science environment and ML pipelines will be covered next, before moving on to building an enterprise ML architecture using Amazon Web Services (AWS). You’ll also learn about security and governance considerations, advanced ML engineering techniques, and how to apply bias detection, explainability, and privacy in ML model development. By the end of this book, you’ll be able to design and build an ML platform to support common use cases and architecture patterns like a true professional.
Table of Contents (17 chapters)
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Hands-on exercise

In this hands-on exercise, we will build a Jupyter Notebook environment on your local machine and build and train an ML model in your local environment. The goal of the exercise is to get some familiarity with the installation process of setting up a local data science environment, and learn how to analyze the data, prepare the data, and train an ML model using one of the algorithms we covered in the preceding sections. First, let's take a look at the problem statement.

Problem statement

Before we start, let's first review the business problem that we need to solve. A retail bank is experiencing a high customer churn rate for its retail banking business. To proactively implement preventive measures to reduce potential churn, the bank needs to know who the potential churners are, so the bank can target those customers with incentives directly to prevent them from leaving. From a business operation perspective, it is much more expensive to acquire...