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 exercise – building an MLOps pipeline on AWS

In this hands-on exercise, you will get hands on with building a simplified version of the enterprise MLOps pipeline. For simplicity, we will not be using the multi-account architecture for the enterprise pattern. Instead, we will build several core functions in a single AWS account. The following diagram shows what you will be building:

Figure 9.10 – Architecture of the hands-on exercise

At a high level, you will create two pipelines using CloudFormation: one for model training and one for model deployment.

Creating a CloudFormation template for the ML training pipeline

In this section, we will create two CloudFormation templates that do the following:

  • The first template creates AWS Step Functions for an ML model training workflow that performs data processing, model training, and model registration. This will be a component of the training pipeline.
  • The second template...