Hands-On Artificial Intelligence on Amazon Web Services
图书信息
| 作者 | Subhashini Tripuraneni |
| 出版社 | Packt Publishing |
| ISBN | 9781789531473 |
| 出版时间 | 2019-10-04 |
| 字数 | 49.3万 |
| 分类 | 进口书,外文原版书,电脑,网络 |
读书简介
Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features * Explore popular machine learning and deep learning services with their underlying algorithms * Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services * Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn * Gain useful insights into different machine and deep learning models * Build and deploy robust deep learning systems to production * Train machine and deep learning models with diverse infrastructure specifications * Scale AI apps without dealing with the complexity of managing the underlying infrastructure * Monitor and Manage AI experiments efficiently * Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.
目录
About Packt
Why subscribe?
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Introduction and Anatomy of a Modern AI Application
Introduction to Artificial Intelligence on Amazon Web Services
Technical requirements
What is AI?
Applications of AI
Autonomous vehicles
AI in medical care
Personalized predictive keyboards
Why use Amazon Web Services for AI?
Overview of AWS AI offerings
Hands-on with AWS services
Creating your AWS account
Navigating through the AWS Management Console
Finding AWS services
Choosing the AWS region
Test driving the Amazon Rekognition service
Working with S3
Identity and Access Management
Getting familiar with the AWS CLI
Installing Python
Installing Python on macOS
Installing Python on Linux
Installing Python on Microsoft Windows
Windows 10
Earlier Windows versions
Installing the AWS CLI
Configuring the AWS CLI
Invoking the Rekognition service using the AWS CLI
Using Python for AI applications
Setting up a Python development environment
Setting up a Python virtual environment with Pipenv
Creating your first Python virtual environment
First project with the AWS SDK
Summary
References
Anatomy of a Modern AI Application
Technical requirements
Understanding the success factors of artificial intelligence applications
Understanding the architecture design principles for AI applications
Understanding the architecture of modern AI applications
Creation of custom AI capabilities
Working with a hands-on AI application architecture
Object detector architecture
Component interactions of the Object Detector
Creating the base project structure
Developing an AI application locally using AWS Chalice
Developing a demo application web user interface
Deploying AI application backends to AWS via Chalice
Deploying a static website via AWS S3
Summary
Further reading
Section 2: Building Applications with AWS AI Services
Detecting and Translating Text with Amazon Rekognition and Translate
Making the world smaller
Understanding the architecture of Pictorial Translator
Component interactions of Pictorial Translator
Setting up the project structure
Implementing services
Recognition service – text detection
Translation service – translating text
Storage service – uploading files
A recommendation on unit testing
Implementing RESTful endpoints
Translate the image text endpoint
Upload the image endpoint
Implementing the web user interface
index.html
scripts.js
Deploying Pictorial Translator to AWS
Discussing project enhancement ideas
Summary
Further reading
Performing Speech-to-Text and Vice Versa with Amazon Transcribe and Polly
Technical requirements
Technologies from science fiction
Understanding the architecture of Universal Translator
Component interactions of Universal Translator
Setting up the project structure
Implementing services
Transcription service – speech-to-text
Translation Service – translating text
Speech Service – text-to-speech
Storage Service – uploading and retrieving a file
Implementing RESTful endpoints
Translate recording endpoint
Synthesize speech endpoint
Upload recording Endpoint
Implementing the Web User Interface
index.html
scripts.js
Deploying the Universal Translator to AWS
Discussing the project enhancement ideas
Summary
References
Extracting Information from Text with Amazon Comprehend
Technical requirements
Working with your Artificial Intelligence coworker
Understanding the Contact Organizer architecture
Component interactions in Contact Organizer
Setting up the project structure
Implementing services
Recognition Service – text detection
Extraction Service – contact information extraction
Contact Store – save and retrieve contacts
Storage Service – uploading and retrieving a file
Implementing RESTful endpoints
Extract Image Information endpoint
Save contact and get all contacts endpoints
Upload image endpoint
Implementing the web user interface
Index.html
scripts.js
Deploying the Contact Organizer to AWS
Discussing the project enhancement ideas
Summary
Further reading
Building a Voice Chatbot with Amazon Lex
Understanding the friendly human-computer interface
Contact assistant architecture
Understanding the Amazon Lex development paradigm
Setting up the contact assistant bot
The LookupPhoneNumberByName intent
Sample utterances and slots for LookupPhoneNumberByName
Confirmation prompt and response for LookupPhoneNumberByName
Fulfillment for LookupPhoneNumberByName using AWS Lambda
DynamoDB IAM role for LookupPhoneNumberByName
Fulfillment lambda function for LookupPhoneNumberByName
Amazon Lex helper functions
The intent fulfillment for LookupPhoneNumberByName
Test conversations for LookupPhoneNumberByName
The MakePhoneCallByName intent
Sample utterances and lambda initialization/validation for MakePhoneCallByName
Slots and confirmation prompt for MakePhoneCallByName
Fulfillment and response for MakePhoneCallByName
Test conversations for MakePhoneCallByName
Deploying the contact assistant bot
Integrating the contact assistant into applications
Intelligent assistant service implementation
Contact assistant RESTful endpoint
Summary
Further reading
Section 3: Training Machine Learning Models with Amazon SageMaker
Working with Amazon SageMaker
Technical requirements
Preprocessing big data through Spark EMR
Conducting training in Amazon SageMaker
Learning how Object2Vec Works
Training the Object2Vec algorithm
Deploying the trained Object2Vec and running inference
Running hyperparameter optimization (HPO)
Understanding the SageMaker experimentation service
Bring your own model – SageMaker, MXNet, and Gluon
Bring your own container – R model
Summary
Further reading
Creating Machine Learning Inference Pipelines
Technical requirements
Understanding the architecture of the inference pipeline in SageMaker
Creating features using Amazon Glue and SparkML
Walking through the prerequisites
Preprocessing data using PySpark
Creating an AWS Glue job
Identifying topics by training NTM in SageMaker
Running online versus batch inferences in SageMaker
Creating real-time predictions through an inference pipeline
Creating batch predictions through an inference pipeline
Summary
Further reading
Discovering Topics in Text Collection
Technical requirements
Reviewing topic modeling techniques
Understanding how the Neural Topic Model works
Training NTM in SageMaker
Deploying the trained NTM model and running the inference
Summary
Further reading
Classifying Images Using Amazon SageMaker
Technical requirements
Walking through convolutional neural and residual networks
Classifying images through transfer learning in Amazon SageMaker
Creating input for image classification
Defining hyperparameters for image classification
Performing inference through Batch Transform
Summary
Further reading
Sales Forecasting with Deep Learning and Auto Regression
Technical requirements
Understanding traditional time series forecasting
Auto-Regressive Integrated Moving Average (ARIMA )
Exponential smoothing
How the DeepAR model works
Model architecture
Arriving at optimal network weights
Understanding model sales through DeepAR
Brief description of the dataset
Exploratory data analysis
Data pre-processing
Training DeepAR
Predicting and evaluating sales
Summary
Further reading
Section 4: Machine Learning Model Monitoring and Governance
Model Accuracy Degradation and Feedback Loops
Technical requirements
Monitoring models for degraded performance
Developing a use case for evolving training data – ad-click conversion
Creating a machine learning feedback loop
Exploring data
Creating features
Using Amazon's SageMaker XGBoost algorithm to classify ad-click data
Evaluating model performance
Summary
Further reading
What Is Next?
Summarizing the concepts we learned in Part I
Summarizing the concepts we learned in Part II
Summarizing the concepts we learned in Part III
Summarizing the concepts we learned in Part IV
What's next?
Artificial intelligence in the physical world
AWS DeepLens
AWS DeepRacer
Internet of Things and AWS IoT Greengrass
Artificial intelligence in your own field
Summary
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