Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Foreword -- Contributors -- Table of Contents -- Preface -- Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS -- Chapter 1: Getting Started with Automated Machine Learning on AWS -- Technical requirements -- Overview of the ML process -- Complexities in the ML process -- An example of the end-to-end ML process -- Introducing ACME Fishing Logistics -- The case for ML -- Getting insights from the data -- Building the right model -- Training the model -- Evaluating the trained model -- Exploring possible next steps -- Tuning our model -- Deploying the optimized model into production -- Streamlining the ML process with AutoML -- How AWS makes automating the ML development and deployment process easier -- Summary -- Chapter 2: Automating Machine Learning Model Development Using SageMaker Autopilot -- Technical requirements -- Introducing the AWS AI and ML landscape -- Overview of SageMaker Autopilot -- Overcoming automation challenges with SageMaker Autopilot -- Getting started with SageMaker Studio -- Preparing the experiment data -- Starting the Autopilot experiment -- Running the Autopilot experiment -- Post-experimentation tasks -- Using the SageMaker SDK to automate the ML experiment -- Codifying the Autopilot experiment -- Analyzing the Autopilot experiment with code -- Deploying the best candidate -- Cleaning up -- Summary -- Chapter 3: Automating Complicated Model Development with AutoGluon -- Technical requirements -- Introducing the AutoGluon library -- Using AutoGluon for tabular data -- Prerequisites -- Creating the AutoML experiment with AutoGluon -- Evaluating the experiment results -- Using AutoGluon for image data -- Prerequisites -- Creating an image prediction experiment -- Evaluating the experiment results -- Summary…”