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 environment using AWS services

In this hands-on exercise, you will create a data science environment using SageMaker with AWS CodeCommit as the source control.

Problem statement

As an ML Solutions Architect, you have been tasked with building a data science environment on AWS for the data scientists in the Equity Research department. The data scientists in the Equity Research department have several NLP problems, such as detecting the sentiment of financial phrases. Once you have created the environment for the data scientists, you also need to build a proof of concept to show the data scientists how to build and train an NLP model using the environment.


The data scientists have indicated that they like to use the BERT model to solve sentiment analysis problems, and they plan to use the financial phrase dataset to establish some initial benchmarks for the model: