Hands-On Artificial Intelligence on Amazon Web Services: Decrease the time to market for AI and ML applications with the power of AWS [Minkštas viršelis]

  • Formatas: Paperback / softback, 426 pages, aukštis x plotis: 92x75 mm
  • Išleidimo metai: 04-Oct-2019
  • Leidėjas: Packt Publishing Limited
  • ISBN-10: 1789534143
  • ISBN-13: 9781789534146
  • Formatas: Paperback / softback, 426 pages, aukštis x plotis: 92x75 mm
  • Išleidimo metai: 04-Oct-2019
  • Leidėjas: Packt Publishing Limited
  • ISBN-10: 1789534143
  • ISBN-13: 9781789534146
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 DescriptionFrom 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 forThis 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 Introduction to Artificial Intelligence on AWS Anatomy of an Modern AI Application Detecting and Translating Text with AWS Rekognition and AWS Translate Performing speech-to-text and Vice-versa with AWS Transcribe and AWS Polly Extracting Information from Text with AWS Comprehend Building a Voice Chatbot with AWS Lex Working with Amazon SageMaker Creating Machine Learning Inference Pipelines with Amazon SageMaker Discovering Topics in Text Collections using Amazon SageMaker Classifying Images using Amazon SageMaker Sales Forecasting with Deep Learning and Auto Regression Model Accuracy Degradation and Feedback Loop What's Next
Subhashini Tripuraneni has several years of experience leading AI initiatives in financial services and convenience retail. She has automated multiple business processes and helped to create a proactive competitive advantage for businesses via AI. She is also a seasoned data scientist, with hands-on experience building machine learning and deep learning models in a public cloud. She holds an MBA from Wharton Business School, with a specialization in business analytics, marketing and operations, and entrepreneurial management. In her spare time, she enjoys going to theme parks and spending time with her children. She currently lives in Dallas, TX, with her husband and children. Charles Song is a solutions architect with a background in applied software engineering research. He is skilled in software development, architecture design, and machine learning, with a proven ability to utilize emerging technologies to devise innovative solutions. He has applied machine learning to many research and industry projects, and published peer-reviewed papers on the subject. He holds a PhD in computer science from the University of Maryland. He has taught several software engineering courses at the University of Maryland for close to a decade. In his spare time, he likes to relax in front of his planted aquariums, but also enjoys martial arts, cycling, and snowboarding. He currently resides in Bethesda, MD, with his wife.