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 – building a data science architecture using open source technologies

In this hands-on exercise, you will build an ML platform using several open source ML platform software. There are three main parts to this hands-on exercise:

  1. Installing Kubeflow and setting up a Kubeflow notebook
  2. Tracking experiments, managing models, and deploying models
  3. Automating the ML steps with Kubeflow Pipelines

Alright, let's get started with the first part – installing Kubeflow on the Amazon EKS cluster.

Part 1 – Installing Kubeflow

You will continue to use the Amazon (EKS) infrastructure you created earlier and install Kubeflow on top of it. To start, let's complete the following steps:

  1. Launch AWS CloudShell: Log in to your AWS account, select the Oregon region, and launch AWS CloudShell again.
  2. Install the kfctl utility: The kfctl utility is a command-line utility for installing and managing Kubeflow. Run the following...