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 considerations for ML

Data management is a broad and complex topic. Many organizations have dedicated data management teams and organizations to manage and govern the various aspects of a data platform. Traditionally, the main focus of data management has been meeting the needs of transactional systems or analytics systems. With the growing adoption of ML solutions, there are new business and technology considerations for data management platforms.

To understand where data management intersects with the ML workflow, let's bring back the ML life cycle, as shown in the following figure:

Figure 4.1 – Intersection of data management and the ML life cycle

At a high level, data management intersects with the ML life cycle in three stages: data understanding and preparation, model training and evaluation, and model deployment.

During the data understanding and preparation stage, data scientists will need to identify data sources...