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

Chapter 9: Amazon SageMaker Modeling

In the previous chapter, we learned several methods of model optimization and evaluation techniques. We also learned various ways of storing data, processing data, and applying different statistical approaches to data. So, how can we now build a pipeline for this? Well, we can read data, process data, and build machine learning models on the processed data. But what if my first machine learning model does not perform well? Can I fine-tune my model? The answer is Yes; you can perform nearly everything using Amazon SageMaker. In this chapter, we will walk you through the following topics using Amazon SageMaker:

  • Understanding different instances of Amazon SageMaker
  • Cleaning and preparing data in Jupyter Notebook in Amazon SageMaker
  • Model training in Amazon SageMaker
  • Using SageMaker's built-in machine learning algorithms
  • Writing custom training and inference code in SageMaker