"Monetizing Machine Learning" is a book that provides guidance on how to turn Python machine learning (ML) ideas into web applications on the serverless cloud. The book is co-authored by Manuel Amunategui and Mehdi Roopaei.
The book starts with an introduction to machine learning and its applications, followed by an overview of serverless computing and its advantages. The authors then delve into the process of turning a machine learning model into a web application, starting with data collection and preprocessing, model selection and training, and deployment on a serverless cloud platform.
The book provides detailed instructions on how to use popular serverless cloud platforms such as AWS Lambda, Google Cloud Functions, and Azure Functions to deploy and run machine learning models. The authors also cover topics such as API design, authentication and authorization, and handling large volumes of data.
One of the key focuses of the book is on how to monetize machine learning applications. The authors discuss various business models, including pay-per-use, subscription, and freemium, and provide guidance on how to implement these models in a machine learning context.
Overall, "Monetizing Machine Learning" is a useful resource for developers, data scientists, and entrepreneurs who want to turn their machine learning ideas into profitable web applications. The book provides practical guidance on how to design, develop, and deploy machine learning applications on the serverless cloud, as well as insights into the business aspects of monetizing these applications.
