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 lab – running ML tasks using AI services

In this hands-on lab, you will perform a list of ML tasks using Rekognition, Comprehend, Textract, and Transcribe. Follow these steps to get started:

  1. Launch the SageMaker Studio profile you created in Chapter 8, Building a Data Science Environment Using AWS ML Services. You will create and run new notebooks in this profile.
  2. We need to provide the new notebooks with permission to access AI services. To do this, find the Studio execution role for the Studio environment and attach the AdministratorAccess IAM policy to it. We will use this policy for simplicity here. In a controlled environment, you would need to design a policy to provide the specific permissions needed to access different services.
  3. Clone into your Studio environment using the git clone