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

Processing real-time data using Kinesis data streams

Kinesis is Amazon's streaming service and can be scaled based on requirements. It is highly available in a region. It has a level of persistence that retains data for 24 hours by default or optionally up to 365 days. Kinesis data streams are used for large-scale data ingestion, analytics, and monitoring:

  • Kinesis can be ingested by multiple producers and multiple consumers can also read data from the stream. Let's understand this by means of an example in real time. Suppose you have a producer ingesting data to a Kinesis stream and the default retention period is 24 hours, which means the data ingested at 05:00:00 A.M. today will be available in the stream until 04:59:59 A.M. tomorrow. This data won't be available beyond that point and ideally, this should be consumed before it expires or can be stored somewhere if it's critical. The retention period can be extended to a maximum of 365 days at an extra...