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 – detecting bias, model explainability, and training privacy-preserving models

Building a comprehensive system for ML governance is a complex task. In this hands-on lab, you will learn how to use some of SageMaker's built-in functionality to support certain aspects of ML governance.

Overview of the scenario

As an ML SA, you have been asked to identify technology solutions that support a project that has regulatory implications. Specifically, you need to determine the technical approaches for data bias detection, model explainability, and privacy-preserving model training. Follow these steps to get started.

Detecting bias in the training dataset

Let's start the hands-on lesson:

  1. Launch the SageMaker Studio environment:
    1. Launch the same SageMaker Studio environment that you have been using.
    2. Create a new folder called chapter11. This will be our working directory for this lab. Create a new Jupyter notebook and name it bias_explainability...