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)
1
Section 1: Solving Business Challenges with Machine Learning Solution Architecture
4
Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
9
Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms

ML use cases in healthcare and life sciences

Healthcare and life science is one of the largest and most complex industries. Within this industry, there are several sectors, including the following:

  • Drugs: These are the drug manufacturers, such as biotechnology firms, pharmaceutical firms, and the makers of genetics drugs.
  • Medical equipment: These are the companies that manufacture both standard products as well as hi-tech equipment.
  • Managed healthcare: These are the companies that provide health insurance policies.
  • Health facilities: These are the hospitals, clinics, and labs.
  • Government agencies such as CDC and FDA.

The industry has adopted ML for a wide range of use cases, such as medical diagnosis and imaging, drug discovery, medical data analysis and management, and disease prediction and treatment.

Medical imaging analysis

Medical imaging is the process and technique of creating a visual representation of the human body for medical analysis...