Navigating Machine Learning Frameworks and APIs

This chapter examines the complexities of selecting appropriate machine learning frameworks and APIs for deploying models, emphasizing the significance of interoperability through tools like ONNX. It discusses personalization trends in user experiences and the potential of federated learning to enhance privacy while training on individual user data. The narrative also explores emerging technologies, such as TinyML and innovative hardware solutions, to optimize machine learning in constrained environments.