Book description
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 exam tips
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.
Table of contents
- AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
- Second Edition
- Contributors
- About the Authors
- About the Reviewer
- Preface
-
Chapter 1: Machine Learning Fundamentals
- Making The Most Out of This Book – Your Certification and Beyond
- Comparing AI, ML, and DL
- Classifying supervised, unsupervised, and reinforcement learning
- The CRISP-DM modeling life cycle
- Data splitting
- Modeling expectations
- Introducing ML frameworks
- ML in the cloud
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
-
Chapter 2: AWS Services for Data Storage
- Technical requirements
- Storing Data on Amazon S3
- Controlling access to buckets and objects on Amazon S3
- Protecting data on Amazon S3
- 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
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
-
Chapter 3: AWS Services for Data Migration and Processing
- Technical requirements
- Creating ETL jobs on AWS Glue
- 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
- Processing stored data on AWS
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
-
Chapter 4: Data Preparation and Transformation
- Identifying types of features
- Dealing with categorical features
- Dealing with numerical features
- Understanding data distributions
- Handling missing values
- Dealing with outliers
- Dealing with unbalanced datasets
- Dealing with text data
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
- Chapter 5: Data Understanding and Visualization
-
Chapter 6: Applying Machine Learning Algorithms
- Introducing this chapter
- Storing the training data
- A word about ensemble models
- Supervised learning
- Unsupervised learning
- Textual analysis
- Image processing
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
- Chapter 7: Evaluating and Optimizing Models
-
Chapter 8: AWS Application Services for AI/ML
- Technical requirements
- Analyzing images and videos with Amazon Rekognition
- Text to speech with Amazon Polly
- Speech to text with Amazon Transcribe
- Implementing natural language processing with Amazon Comprehend
- Translating documents with Amazon Translate
- Extracting text from documents with Amazon Textract
- Creating chatbots on Amazon Lex
- Amazon Forecast
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
-
Chapter 9: Amazon SageMaker Modeling
- Technical requirements
- Creating notebooks in Amazon SageMaker
- Model tuning
- Choosing instance types in Amazon SageMaker
- Taking care of Scalability Configurations
- Securing SageMaker notebooks
- SageMaker Debugger
- SageMaker Autopilot
- SageMaker Model Monitor
- SageMaker Training Compiler
- SageMaker Data Wrangler
- SageMaker Feature Store
- SageMaker Edge Manager
- SageMaker Canvas
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
-
Chapter 10: Model Deployment
- Factors influencing model deployment options
- SageMaker deployment options
- Creating alternative pipelines with Lambda Functions
- Working with step functions
- Scaling applications with SageMaker deployment and AWS Autoscaling
- Securing SageMaker applications
- Summary
- Exam Readiness Drill – Chapter Review Questions
- Working On Timing
- Chapter 11: Accessing the Online Practice Resources
- Other Books You May Enjoy
Product information
- Title: AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition
- Author(s):
- Release date: February 2024
- Publisher(s): Packt Publishing
- ISBN: 9781835082201
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