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

Data management architecture for ML

Depending on the scope of your ML initiatives, you may want to consider different data management architecture patterns to support them.

For small-scale ML projects with limited data scope, team size, and cross-functional dependencies, consider purpose-built data pipelines that meet the project's specific needs. For example, suppose you only need to work with structured data from an existing data warehouse and a dataset from the public domain. In that case, you want to consider building a simple data pipeline that extracts the required data from the data warehouse and the public domain to a storage location owned by the project team on an as-needed schedule for further analysis and processing. The following figure shows a simple data management flow to support a small-scope ML project:

Figure 4.2 – Data architecture for an ML project with limited scope

For large, enterprise-wide ML initiatives, the data...