Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling


In this chapter, we learned about the usage of SageMaker for creating notebook instances and training instances. As we went through we learned how to use SageMaker for hyperparameter tuning jobs. As the security of our assets in AWS is an essential part, we learned about the various ways to secure SageMaker instances. With hands-on practices, we created Step Functions and orchestrated our pipeline using AWS Lambda.

AWS products are evolving every day to help us solve our IT problems. It's not easy to remember all the product names. The only way to learn is through practice. When you're solving a problem or building a product, then focus on the different technological areas of your product. Those areas can be an AWS service, for example, scheduling jobs, logging, tracing, monitoring metrics, autoscaling, and more.

Compute time, storage, and networking are the baselines. It is recommended that you practice some examples for each of these services. Referring to...