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

Introducing model evaluation

There are several different scenarios in which we might want to evaluate model performance, some of them are as follows.

  • You are creating a model and testing different approaches and/or algorithms. Therefore, you need to compare these models to select the best one.
  • You have just completed your model and you need to document your work, which includes specifying the model's performance metrics that you have reached out to during the modeling phase.
  • Your model is running in a production environment and you need to track its performance. If you encounter model drift, then you might want to retrain the model.

    Important note

    The term model drift is used to refer to the problem of model deterioration. When you are building a machine learning model, you must use data to train the algorithm. This set of data is known as training data, and it reflects the business rules at a particular point in time. If these business rules change over time, your...