AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

by Somanath Nanda, Weslley Moura
Released February 2024
Publisher(s): Packt Publishing
ISBN: 9781835082201

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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

  1. AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
  2. Second Edition
  3. Contributors
  4. About the Authors
  5. About the Reviewer
  6. Preface
    1. Who This Book Is for
    2. What This Book Covers
    3. How to Use This Book
    4. Online Practice Resources
    5. Download the Color Images
    6. Conventions Used
    7. Get in Touch
    8. Download a Free PDF Copy of This Book
  7. Chapter 1: Machine Learning Fundamentals
    1. Making The Most Out of This Book – Your Certification and Beyond
    2. Comparing AI, ML, and DL
      1. Examining ML
      2. Examining DL
    3. Classifying supervised, unsupervised, and reinforcement learning
      1. Introducing supervised learning
    4. The CRISP-DM modeling life cycle
    5. Data splitting
      1. Overfitting and underfitting
      2. Applying cross-validation and measuring overfitting
      3. Bootstrapping methods
      4. The variance versus bias trade-off
      5. Shuffling your training set
    6. Modeling expectations
    7. Introducing ML frameworks
    8. ML in the cloud
    9. Summary
    10. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    11. Working On Timing
  8. Chapter 2: AWS Services for Data Storage
    1. Technical requirements
    2. Storing Data on Amazon S3
      1. Creating buckets to hold data
      2. Distinguishing between object tags and object metadata
    3. Controlling access to buckets and objects on Amazon S3
      1. S3 bucket policy
    4. Protecting data on Amazon S3
      1. Applying bucket versioning
      2. Applying encryption to buckets
    5. Securing S3 objects at rest and in transit
    6. Using other types of data stores
    7. Relational Database Service (RDS)
    8. Managing failover in Amazon RDS
    9. Taking automatic backups, RDS snapshots, and restore and read replicas
    10. Writing to Amazon Aurora with multi-master capabilities
    11. Storing columnar data on Amazon Redshift
    12. Amazon DynamoDB for NoSQL Database-as-a-Service
    13. Summary
    14. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    15. Working On Timing
  9. Chapter 3: AWS Services for Data Migration and Processing
    1. Technical requirements
    2. Creating ETL jobs on AWS Glue
      1. Features of AWS Glue
      2. Getting hands-on with AWS Glue Data Catalog components
      3. Getting hands-on with AWS Glue ETL components
    3. Querying S3 data using Athena
    4. Processing real-time data using Kinesis Data Streams
    5. Storing and transforming real-time data using Kinesis Data Firehose
    6. Different ways of ingesting data from on-premises into AWS
      1. AWS Storage Gateway
      2. Snowball, Snowball Edge, and Snowmobile
      3. AWS DataSync
      4. AWS Database Migration Service
    7. Processing stored data on AWS
      1. AWS EMR
      2. AWS Batch
    8. Summary
    9. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    10. Working On Timing
  10. Chapter 4: Data Preparation and Transformation
    1. Identifying types of features
    2. Dealing with categorical features
      1. Transforming nominal features
      2. Applying binary encoding
      3. Transforming ordinal features
      4. Avoiding confusion in our train and test datasets
    3. Dealing with numerical features
      1. Data normalization
      2. Data standardization
      3. Applying binning and discretization
      4. Applying other types of numerical transformations
    4. Understanding data distributions
    5. Handling missing values
    6. Dealing with outliers
    7. Dealing with unbalanced datasets
    8. Dealing with text data
      1. Bag of words
      2. TF-IDF
      3. Word embedding
    9. Summary
    10. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    11. Working On Timing
  11. Chapter 5: Data Understanding and Visualization
    1. Visualizing relationships in your data
    2. Visualizing comparisons in your data
    3. Visualizing distributions in your data
    4. Visualizing compositions in your data
    5. Building key performance indicators
    6. Introducing QuickSight
    7. Summary
    8. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    9. Working On Timing
  12. Chapter 6: Applying Machine Learning Algorithms
    1. Introducing this chapter
    2. Storing the training data
    3. A word about ensemble models
    4. Supervised learning
      1. Working with regression models
        1. Introducing regression algorithms
        2. Least squares method
        3. Creating a linear regression model from scratch
        4. Interpreting regression models
        5. Checking adjusted R squared
        6. Regression modeling on AWS
      2. Working with classification models
      3. Forecasting models
        1. Checking the stationarity of time series
        2. Exploring, exploring, and exploring
        3. Understanding DeepAR
      4. Object2Vec
    5. Unsupervised learning
      1. Clustering
        1. Computing K-Means step by step
        2. Defining the number of clusters and measuring cluster quality
        3. Conclusion
      2. Anomaly detection
      3. Dimensionality reduction
        1. Using AWS’s built-in algorithm for PCA
      4. IP Insights
    6. Textual analysis
      1. BlazingText algorithm
      2. Sequence-to-sequence algorithm
      3. Neural Topic Model algorithm
    7. Image processing
      1. Image classification algorithm
      2. Semantic segmentation algorithm
      3. Object detection algorithm
    8. Summary
    9. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    10. Working On Timing
  13. Chapter 7: Evaluating and Optimizing Models
    1. Introducing model evaluation
    2. Evaluating classification models
      1. Extracting metrics from a confusion matrix
      2. Summarizing precision and recall
    3. Evaluating regression models
      1. Exploring other regression metrics
    4. Model optimization
      1. Grid search
    5. Summary
    6. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    7. Working On Timing
  14. Chapter 8: AWS Application Services for AI/ML
    1. Technical requirements
    2. Analyzing images and videos with Amazon Rekognition
      1. Exploring the benefits of Amazon Rekognition
      2. Getting hands-on with Amazon Rekognition
    3. Text to speech with Amazon Polly
      1. Exploring the benefits of Amazon Polly
      2. Getting hands-on with Amazon Polly
    4. Speech to text with Amazon Transcribe
      1. Exploring the benefits of Amazon Transcribe
      2. Getting hands-on with Amazon Transcribe
    5. Implementing natural language processing with Amazon Comprehend
      1. Exploring the benefits of Amazon Comprehend
      2. Getting hands-on with Amazon Comprehend
    6. Translating documents with Amazon Translate
      1. Exploring the benefits of Amazon Translate
      2. Getting hands-on with Amazon Translate
    7. Extracting text from documents with Amazon Textract
      1. Exploring the benefits of Amazon Textract
      2. Getting hands-on with Amazon Textract
    8. Creating chatbots on Amazon Lex
      1. Exploring the benefits of Amazon Lex
      2. Getting hands-on with Amazon Lex
    9. Amazon Forecast
      1. Exploring the benefits of Amazon Forecast
      2. Sales Forecasting Model with Amazon Forecast
    10. Summary
    11. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    12. Working On Timing
  15. Chapter 9: Amazon SageMaker Modeling
    1. Technical requirements
    2. Creating notebooks in Amazon SageMaker
      1. What is Amazon SageMaker?
      2. Training Data Location and Formats
      3. Getting hands-on with Amazon SageMaker notebook instances
      4. Getting hands-on with Amazon SageMaker’s training and inference instances
    3. Model tuning
      1. Tracking your training jobs and selecting the best model
    4. Choosing instance types in Amazon SageMaker
      1. Choosing the right instance type for a training job
      2. Choosing the right instance type for an inference job
    5. Taking care of Scalability Configurations
      1. Scaling Policy Overview
      2. Scale Based on a Schedule
      3. Minimum and Maximum Scaling Limits
      4. Cooldown Period
    6. Securing SageMaker notebooks
    7. SageMaker Debugger
    8. SageMaker Autopilot
    9. SageMaker Model Monitor
    10. SageMaker Training Compiler
    11. SageMaker Data Wrangler
    12. SageMaker Feature Store
    13. SageMaker Edge Manager
    14. SageMaker Canvas
    15. Summary
    16. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    17. Working On Timing
  16. Chapter 10: Model Deployment
    1. Factors influencing model deployment options
    2. SageMaker deployment options
      1. Real-time endpoint deployment
        1. Solution
        2. Steps
        3. Example code snippet
      2. Batch transform job
        1. Solution
        2. Steps
        3. Example code snippet
      3. Multi-model endpoint deployment
        1. Solution
        2. Steps
        3. Example code snippet
      4. Endpoint autoscaling
        1. Solution
        2. Steps
        3. Example code snippet
      5. Serverless APIs with AWS Lambda and SageMaker
        1. Solution
        2. Steps
        3. Example code snippet
    3. Creating alternative pipelines with Lambda Functions
      1. Creating and configuring a Lambda Function
      2. Completing your configurations and deploying a Lambda function
    4. Working with step functions
    5. Scaling applications with SageMaker deployment and AWS Autoscaling
      1. Scenario 1 – Fluctuating inference workloads
        1. Autoscaling solution
        2. Steps
        3. Example code snippet
      2. Scenario 2 – The batch processing of large datasets
        1. Autoscaling solution
        2. Steps
        3. Example code snippet
      3. Scenario 3 – A multi-model endpoint with dynamic traffic
        1. Autoscaling solution
        2. Steps
        3. Example code snippet
      4. Scenario 4 – Continuous Model Monitoring with drift detection
        1. Autoscaling solution
        2. Steps
    6. Securing SageMaker applications
    7. Summary
    8. Exam Readiness Drill – Chapter Review Questions
      1. Exam Readiness Drill
        1. ATTEMPT 1
        2. ATTEMPT 2
        3. ATTEMPT 3
    9. Working On Timing
  17. Chapter 11: Accessing the Online Practice Resources
    1. How to Access These Resources
      1. Purchased from Packt Store (packtpub.com)
      2. Packt+ Subscription
      3. Purchased from Amazon and Other Sources
        1. STEP 1
        2. STEP 2
        3. STEP 3
        4. STEP 4
        5. STEP 5
    2. Troubleshooting Tips
    3. Practice Resources – A Quick Tour
      1. A Clean, Simple Cert Practice Experience
      2. Practice Questions
      3. Flashcards
      4. Exam Tips
      5. Chapter Review Questions
      6. Share Feedback
    4. Back to the Book
    5. Why subscribe?
  18. Other Books You May Enjoy
    1. Share Your Thoughts
    2. Download a Free PDF Copy of This Book

Product information

  • Title: AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition
  • Author(s): Somanath Nanda, Weslley Moura
  • Release date: February 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781835082201