Book description
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.
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Introduction and Anatomy of a Modern AI Application
- Introduction to Artificial Intelligence on Amazon Web Services
-
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
- Developing an AI application locally using AWS Chalice
- Developing a demo application web user interface
- 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 
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the web user interface
- 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
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the Web User Interface
- 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
- Setting up the project structure
- Implementing services
- Implementing RESTful endpoints
- Implementing the web user interface
- 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
- Fulfillment lambda function for LookupPhoneNumberByName
- Amazon Lex helper functions
- The intent fulfillment for LookupPhoneNumberByName
- Test conversations for LookupPhoneNumberByName
- The MakePhoneCallByName intent
- Deploying the contact assistant bot
-
The LookupPhoneNumberByName intent
- Integrating the contact assistant into applications
- 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
- 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
- Discovering Topics in Text Collection
- Classifying Images Using Amazon SageMaker
- Sales Forecasting with Deep Learning and Auto Regression
- Section 4: Machine Learning Model Monitoring and Governance
- Model Accuracy Degradation and Feedback Loops
- What Is Next?
- Other Books You May Enjoy
Product information
- Title: Hands-On Artificial Intelligence on Amazon Web Services
- Author(s):
- Release date: October 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789534146
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