Читать книгу AWS Certified Machine Learning Study Guide - Shreyas Subramanian - Страница 13

Machine learning (ML) is one of the most popular and rapidly growing fields in the technology industry today, with far-reaching business implications. The market for ML solutions and products is expected to grow annually by tens of billions of dollars, and with it, the demand for professionals who understand how to analyze data and build ML solutions is expected to grow as well.

ML is a highly technical field, and successful ML professionals need a foundation in mathematics, statistics, and data analysis. They must be able to code and have a fundamental understanding of infrastructure and software development best practices. In the past, the practitioners of machine learning were academics and PhDs, but the industry demand for ML is much larger than the supply of new PhDs emerging from academic institutions.

The purpose of this book is for you to understand the concepts and principles behind ML, with the practical goal of passing the AWS Certified Machine Learning Specialty exam. As practicing ML solution architects, we go well beyond the scope of the test in this book and incorporate architecture patterns and best practices that we have seen employed in the industry today. Reading this book will also give you an understanding of what is required to be a successful machine learning architect.

This is not a book on ML foundations. That is simply too vast a field for us to do it justice in this book and also is not our intention. There are a number of excellent textbooks and online resources you can use to develop a foundation on ML algorithms, deep learning, and similar topics. However, we will cover the concepts that you will need for the test.

Finally, one of our favorite leadership principles here at Amazon that widely applies to the solution architect role is learn and be curious. We have found that the best way to learn a topic is to get hands-on, and we highly recommend that you go beyond this book and get hands-on experience in ML. Download and explore some public datasets, and train some simple predictive models. Build a neural network from scratch using TensorFlow/PyTorch or just native Python. Explore AWS services such as Amazon SageMaker by running some of the sample Jupyter Notebooks. We highly recommend getting some hands-on knowledge before taking the test. Check out the AWS Training and Certification web page for helpful courses: www.aws.training.

Don't just study the questions and answers! The questions on the actual exam will be different from the practice questions included in this book. The exam is designed to test your knowledge of a concept or objective, so use this book to learn the objectives behind the questions.

The ML space is maturing and growing very quickly; what this means is that our book is just a snapshot in time of our understanding of the industry and certification requirements. We highly recommend that you read the SageMaker home page to review the latest releases that may appear on the test.