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

Model tuning

In Chapter 8, Evaluating and Optimizing Models, you learned many important concepts about model tuning. Let's now explore this topic from a practical perspective.

In order to tune a model on SageMaker, we have to call create_hyper_parameter_tuning_job and pass the following main parameters:

  • HyperParameterTuningJobName: This is the name of the tuning job. It is useful to track the training jobs that have been started on behalf of your tuning job.
  • HyperParameterTuningJobConfig: Here, you can configure your tuning options. For example, which parameters you want to tune, the range of values for them, the type of optimization (such as random search or Bayesian search), the maximum number of training jobs you want to spin up, and more.
  • TrainingJobDefinition: Here, you can configure your training job. For example, the data channels, the output location, the resource configurations, the evaluation metrics, and the stop conditions.

In SageMaker, the...