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)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

Hands-on – creating a Kubernetes infrastructure on AWS

In this section, you will create a Kubernetes environment using the Amazon EKS. Let's first look at the problem statement in the following section.

Problem statement

As a ML solutions architect, you have been tasked to evaluate Kubernetes as a potential infrastructure platform for building an ML platform for one business unit in your bank. You need to build a sandbox environment on AWS and demonstrate that you can deploy a Jupyter Notebook as a containerized application for your data scientists to use.

Lab instruction

In this hands-on exercise, you are going to create a Kubernetes environment using the Amazon EKS. The EKS is a managed service for Kubernetes on AWS that creates and configures a Kubernetes cluster with both master and worker nodes automatically. The EKS provisions and scales the control plane, including the API server and backend persistent layer. The EKS runs the open source Kubernetes and...