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 media and entertainment

The media and entertainment (M&E) industry consists of businesses that engage in the production and distribution of films, television, streaming content, music, games, and publishing. The current M&E landscape has been shaped by the increasing adoption of streaming and over-the-top (OTT) content delivery versus traditional broadcasting. M&E customers, faced with ever-increasing media content choices, are shifting their consumption habits and demanding more personalized and enhanced experiences across different devices, anytime, anywhere. M&E companies are also faced with fierce competition in the industry, and to stay competitive, M&E companies need to identify new monetization channels, improve user experience, and improve operational efficiency. The following diagram shows the main steps in the media production and distribution workflow:

Figure 2.10 – Media production and distribution workflow...