Book Image

Hands-On Artificial Intelligence on Amazon Web Services

By : Subhashini Tripuraneni, Charles Song
1 (1)
Book Image

Hands-On Artificial Intelligence on Amazon Web Services

1 (1)
By: Subhashini Tripuraneni, Charles Song

Overview of this book

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.
Table of Contents (19 chapters)
Free Chapter
1
Section 1: Introduction and Anatomy of a Modern AI Application
4
Section 2: Building Applications with AWS AI Services
9
Section 3: Training Machine Learning Models with Amazon SageMaker
15
Section 4: Machine Learning Model Monitoring and Governance

Summary

In this chapter, you've learned how to process big data to create an analytics-ready dataset. You've also seen how SageMaker automates most of the steps of the machine learning life cycle, enabling you to build, train, and deploy models seamlessly. Additionally, we've illustrated some of the productivity features, such as hyperparameter optimization and experimentation service, which enable data scientists to run multiple experiments and deploy the winning model. Finally, we have looked at bringing our own models and containers to the SageMaker ecosystem. Through bringing our own models based on open source machine learning libraries, we can readily build solutions based on open source frameworks, while still leveraging all of the capabilities of the platform. Similarly, by bringing our own container, we can readily port solutions, written in other programming...