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

ML use cases in retail

Retail businesses sell consumer products directly to customers through retail stores or e-commerce channels. They get supplies through wholesale distributors or from manufacturers directly. The industry has been going through some significant transformations. While e-commerce is growing much faster than traditional retail business, traditional brick-and-mortar stores are also transforming in-store shopping experiences to stay competitive. Retailers are looking for new ways to improve the overall shopping experience through both online and physical channels. New trends such as social commerce, augmented reality, virtual assistant shopping, smart stores, and 1:1 personalization are becoming some of the key differentiators among retail businesses.

AI and ML are a key driving force behind the retail industry's transformation, from inventory optimization and demand forecasting to highly personalized and immersive shopping experiences such as personalized...