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)
1
Section 1: Introduction to Machine Learning
4
Section 2: Data Engineering and Exploratory Data Analysis
9
Section 3: Data Modeling

Building key performance indicators

Before we wrap up these data visualization sections, I want to introduce key performance indicators, or KPIs for short.

A KPI is usually a single value that describes the results of a business indicator, such as the churn rate, net promoter score (NPS), return on investment (ROI), and so on. Although there are some commonly used indicators across different industries, you are free to come up with a number, based on your company's needs.

To be honest, the most complex challenge associated with indicators is not in their visualization aspect itself, but in the way they have been built (the rules used) and the way they will be communicated and used across different levels of the company.

From a visualization perspective, just like any other single value, you can use all those charts that we have learned about to analyze your indicator, depending on your need. However, if you just want to show your KPI, with no time dimension, you can...