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

Querying S3 data using Athena

Athena is a serverless service designed for querying data stored in S3. It is serverless because the client doesn't manage the servers that are used for computation:

  • Athena uses a schema to present the results against the query on the data stored in S3. You define how you want your data to appear in the form of a schema and Athena reads the raw data from S3 to show the results as per the defined schema.
  • The output can be used by other services for visualization, storing, or various analytics purposes. The source data in S3 can be in any of the following structured, semi-structured, and unstructured data formats, including XML, JSON, CSV/TSV, AVRO, Parquet, ORC, and more. CloudTrail, ELB Logs, and VPC flow logs can also be stored in S3 and analyzed by Athena.
  • This follows the schema-on-read technique. Unlike traditional techniques, tables are defined in advance in a data catalog, and data is projected when it reads. SQL-like queries...