AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide : The Ultimate Guide to Passing the MLS-C01 Exam on Your First Attempt / Somanath Nanda and Weslley Moura.

Format:

Book

Author/Creator:

Nanda, Somanath, author.

Moura, Weslley, author.

Language:

English

Subjects (All):

Amazon Web Services (Firm)--Examinations--Study guides.

Amazon Web Services (Firm).

Machine learning--Examinations--Study guides.

Machine learning.

Cloud computing--Examinations--Study guides.

Cloud computing.

Artificial intelligence--Examinations--Study guides.

Artificial intelligence.

Physical Description:

1 online resource (343 pages)

Edition:

Second edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2024]

Biography/History:

Nanda Somanath: Somanath has 10 years of working experience in IT industry which includes Prod development, Devops, Design and architect products from end to end. He has also worked at AWS as a Big Data Engineer for about 2 years. Moura Weslley: Weslley Moura has been developing data products for the past decade. At his recent roles, he has been influencing data strategy and leading data teams into the urban logistics and blockchain industries.

Summary:

Prepare confidently for the AWS MLS-C01 certification with this comprehensive and up-to-date exam guide, accompanied by web-based tools such as mock exams, flashcards, and hands-on activities Key Features Gain proficiency in AWS machine learning services to excel in the MLS-C01 exam Build model training and inference pipelines and deploy machine learning models to the AWS cloud Practice on the go with the mobile-friendly bonus website, accessible with the book Purchase of the print or Kindle book includes a free PDF eBook Book Description The AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices--PCs, tablets, and smartphones. Throughout the book, you'll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification. What you will learn Identify ML frameworks for specific tasks Apply CRISP-DM to build ML pipelines Combine AWS services to build AI/ML solutions Apply various techniques to transform your data, such as one-hot encoding, binary encoder, ordinal encoding, binning, and text transformations Visualize relationships, comparisons, compositions, and distributions in the data Use data preparation techniques and AWS services for batch and real-time data processing Create training and inference ML pipelines with Sage Maker Deploy ML models in a production environment efficiently Who this book is for This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book.

Contents:

Cover

FM

Copyright

Contributors

Table of Contents

Preface

Chapter 1: Machine Learning Fundamentals

Making the Most Out of this Book - Your Certification and Beyond

Comparing AI, ML, and DL

Examining ML

Examining DL

Classifying supervised, unsupervised, and reinforcement learning

Introducing supervised learning

The CRISP-DM modeling life cycle

Data splitting

Overfitting and underfitting

Applying cross-validation and measuring overfitting

Bootstrapping methods

The variance versus bias trade-off

Shuffling your training set

Modeling expectations

Introducing ML frameworks

ML in the cloud

Summary

Exam Readiness Drill - Chapter Review Questions

Chapter 2: AWS Services for Data Storage

Technical requirements

Storing Data on Amazon S3

Creating buckets to hold data

Distinguishing between object tags and object metadata

Controlling access to buckets and objects on amazon s3

S3 bucket policy

Protecting data on amazon s3

Applying bucket versioning

Applying encryption to buckets

Securing s3 objects at rest and in transit

Using other types of data stores

Relational Database Service (RDS)

Managing failover in Amazon RDS

Taking automatic backups, RDS snapshots, and restore and read replicas

Writing to Amazon Aurora with multi-master capabilities

Storing columnar data on Amazon Redshift

Amazon DynamoDB for NoSQL Database-as-a-Service

Chapter 3: AWS Services for Data Migration and Processing

Creating ETL jobs on AWS Glue

Features of AWS Glue

Getting hands-on with AWS Glue Data Catalog components

Getting hands-on with AWS Glue ETL components

Querying S3 data using Athena

Processing real-time data using Kinesis Data Streams.

Storing and transforming real-time data using Kinesis Data Firehose

Different ways of ingesting data from on-premises into AWS

AWS Storage Gateway

Snowball, Snowball Edge, and Snowmobile

AWS DataSync

AWS Database Migration Service

Processing stored data on AWS

AWS EMR

AWS Batch

Chapter 4: Data Preparation and Transformation

Identifying types of features

Dealing with categorical features

Transforming nominal features

Applying binary encoding

Transforming ordinal features

Avoiding confusion in our train and test datasets

Dealing with numerical features

Data normalization

Data standardization

Applying binning and discretization

Applying other types of numerical transformations

Understanding data distributions

Handling missing values

Dealing with outliers

Dealing with unbalanced datasets

Dealing with text data

Bag of words

TF-IDF

Word embedding

Chapter 5: Data Understanding and Visualization

Visualizing relationships in your data

Visualizing comparisons in your data

Visualizing distributions in your data

Visualizing compositions in your data

Building key performance indicators

Introducing QuickSight

Chapter 6: Applying Machine Learning Algorithms

Introducing this chapter

Storing the training data

A word about ensemble models

Supervised learning

Working with regression models

Introducing regression algorithms

Least squares method

Creating a linear regression model from scratch

Interpreting regression models

Checking adjusted R squared

Regression modeling on AWS

Working with classification models

Forecasting models.

Checking the stationarity of time series

Exploring, exploring, and exploring

Understanding DeepAR

Object2Vec

Unsupervised learning

Clustering

Computing K-Means step by step

Defining the number of clusters and measuring cluster quality

Conclusion

Anomaly detection

Dimensionality reduction

Using AWS's built-in algorithm for PCA

IP Insights

Textual analysis

BlazingText algorithm

Sequence-to-sequence algorithm

Neural Topic Model algorithm

Image processing

Image classification algorithm

Semantic segmentation algorithm

Object detection algorithm

Chapter 7: Evaluating and Optimizing Models

Introducing model evaluation

Evaluating classification models

Extracting metrics from a confusion matrix

Summarizing precision and recall

Evaluating regression models

Exploring other regression metrics

Model optimization

Grid search

Chapter 8: AWS Application Services for AI/ML

Analyzing images and videos with Amazon Rekognition

Exploring the benefits of Amazon Rekognition

Getting hands-on with Amazon Rekognition

Text to speech with Amazon Polly

Exploring the benefits of Amazon Polly

Getting hands-on with Amazon Polly

Speech to text with Amazon Transcribe

Exploring the benefits of Amazon Transcribe

Getting hands-on with Amazon Transcribe

Implementing natural language processing with Amazon Comprehend

Exploring the benefits of Amazon Comprehend

Getting hands-on with Amazon Comprehend

Translating documents with Amazon Translate

Exploring the benefits of Amazon Translate

Getting hands-on with Amazon Translate

Extracting text from documents with Amazon Textract.

Exploring the benefits of Amazon Textract

Getting hands-on with Amazon Textract

Creating chatbots on Amazon Lex

Exploring the benefits of Amazon Lex

Getting hands-on with Amazon Lex

Amazon Forecast

Exploring the benefits of Amazon Forecast

Sales Forecasting Model with Amazon Forecast

Chapter 9: Amazon SageMaker Modeling

Creating notebooks in Amazon SageMaker

What is Amazon SageMaker?

Training Data Location and Formats

Getting hands-on with Amazon SageMaker notebook instances

Getting hands-on with Amazon SageMaker's training and inference instances

Model tuning

Tracking your training jobs and selecting the best model

Choosing instance types in Amazon SageMaker

Choosing the right instance type for a training job

Choosing the right instance type for an inference job

Taking care of Scalability Configurations

Scaling Policy Overview

Scale Based on a Schedule

Minimum and Maximum Scaling Limits

Cooldown Period

Securing SageMaker notebooks

SageMaker Debugger

SageMaker Autopilot

SageMaker Model Monitor

SageMaker Training Compiler

SageMaker Data Wrangler

SageMaker Feature Store

SageMaker Edge Manager

SageMaker Canvas

Chapter 10: Model Deployment

Factors influencing model deployment options

SageMaker deployment options

Real-time endpoint deployment

Solution

Steps

Example code snippet

Batch transform job

Multi-model endpoint deployment

Endpoint autoscaling

Serverless APIs with AWS Lambda and SageMaker

Example code snippet.

Creating alternative pipelines with Lambda Functions

Creating and configuring a Lambda Function

Completing your configurations and deploying a Lambda function

Working with step functions

Scaling applications with SageMaker deployment and AWS Autoscaling

Scenario 1 - Fluctuating inference workloads

Autoscaling solution

Scenario 2 - The batch processing of large datasets

Scenario 3 - A multi-model endpoint with dynamic traffic

Scenario 4 - Continuous Model Monitoring with drift detection

Securing SageMaker applications

Chapter 11: Accessing the Online Practice Resources

Index

Other Books You May Enjoy.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781835082904

1835082904

OCLC:

1424950477