AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide The Ultimate Guide to Passing the MLS-C01 Exam on Your First Attempt
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 trai...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2024]
|
| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805122706719 |
Tabla de Contenidos:
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- Chapter 3: AWS Services for Data Migration and Processing
- Technical requirements
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- Chapter 8: AWS Application Services for AI/ML
- Technical requirements
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- Chapter 9: Amazon SageMaker Modeling
- Technical requirements
- 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
- Summary
- Exam Readiness Drill - Chapter Review Questions
- Chapter 10: Model Deployment
- Factors influencing model deployment options
- SageMaker deployment options
- Real-time endpoint deployment
- Solution
- Steps
- Example code snippet
- Batch transform job
- Solution
- Steps
- Example code snippet
- Multi-model endpoint deployment
- Solution
- Steps
- Example code snippet
- Endpoint autoscaling
- Solution
- Steps
- Example code snippet
- Serverless APIs with AWS Lambda and SageMaker
- Solution
- Steps
- 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
- Steps
- Example code snippet
- Scenario 2 - The batch processing of large datasets
- Autoscaling solution
- Steps
- Example code snippet
- Scenario 3 - A multi-model endpoint with dynamic traffic
- Autoscaling solution
- Steps
- Example code snippet
- Scenario 4 - Continuous Model Monitoring with drift detection
- Autoscaling solution
- Steps
- Securing SageMaker applications
- Summary
- Exam Readiness Drill - Chapter Review Questions
- Chapter 11: Accessing the Online Practice Resources
- Index
- Other Books You May Enjoy.
