Improve a machine learning model by automatically tuning its hyperparameters
Pick the optimal components for creating and improving your pipelines
Use AutoML toolkits such as AutoKeras and KerasTuner
Design and implement search algorithms to find the best component for your ML task
Accelerate the AutoML process with data-parallel, model pretraining, and other techniquesAutomated Machine Learning in Action reveals how you can automate the burdensome elements of designing and tuning your machine learning systems. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. AutoML can even be implemented by machine learning novices! If you’re new to ML, you’ll appreciate how the book primes you on machine learning basics. Experienced practitioners will love learning how automated tools like AutoKeras and KerasTuner can create pipelines that automatically select the best approach for your task, or tune any customized search space with user-defined hyperparameters, which removes the burden of manual tuning.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Machine learning tasks like data pre-processing, feature selection, and model optimization can be time-consuming and highly technical. Automated machine learning, or AutoML, applies pre-built solutions to these chores, eliminating errors caused by manual processing. By accelerating and standardizing work throughout the ML pipeline, AutoML frees up valuable data scientist time and enables less experienced users to apply machine learning effectively.