Code: USD75OFF
This is the code repository for Intelligent Document Processing with AWS AI/ML, published by Packt.
A comprehensive guide to building IDP pipelines with applications across industries
With the volume of data growing exponentially in this digital era, it has become paramount for professionals to process this data in an accelerated and cost-effective manner to get value out of it. Data that organizations receive is usually in raw document format, and being able to process these documents is critical to meeting growing business needs. This book is a comprehensive guide to helping you get to grips with AI/ML fundamentals and their application in document processing use cases. You’ll begin by understanding the challenges faced in legacy document processing and discover how you can build end-to-end document processing pipelines with AWS AI services. As you advance, you'll get hands-on experience with popular Python libraries to process and extract insights from documents. This book starts with the basics, taking you through real industry use cases for document processing to deliver value-based care in the healthcare industry and accelerate loan application processing in the financial industry. Throughout the chapters, you'll find out how to apply your skillset to solve practical problems. By the end of this AWS book, you’ll have mastered the fundamentals of document processing with machine learning through practical implementation
This book covers the following exciting features:
- Understand the requirements and challenges in deriving insights from a document
- Explore common stages in the intelligent document processing pipeline
- Discover how AWS AI/ML can successfully automate IDP pipelines
- Find out how to write clean and elegant Python code by leveraging AI
- Get to grips with the concepts and functionalities of AWS AI services
- Explore IDP across industries such as insurance, healthcare, finance, and the public sector
- Determine how to apply business rules in IDP
- Build, train, and deploy models with serverless architecture for IDP
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
chapter2_syncdensedoc = "syncdensetext.png"
display(Image(url=s3.generate_presigned_url(
'get_object', Params={
'Bucket': s3BucketName, 'Key': chapter2_syncdensedoc
})
))
Following is what you need for this book: This book is for technical professionals and thought leaders who want to understand and solve business problems by leveraging insights from their documents. If you want to learn about machine learning and artificial intelligence, and work with real-world use cases such as document processing with technology, this book is for you. To make the most of this book, you should have basic knowledge of AI/ML and python programming concepts. This book is also especially useful for developers looking to explore AI/ML with industry use cases.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Sign-up with an AWS account | Windows OS |
| 1-10 | Access to a web browser to access AWS | Windows OS |
| 1-10 | Python | Windows OS |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Sonali Sahu is a leading Intelligent Document Processing Artificial Intelligence (AI) and Machine Learning (ML) solutions architect on the team at Amazon Web Services. She is a passionate technophile and enjoys working with customers to solve complex problems using innovation. Her core area of focus is AI and ML. She has both breadth and depth of experience working with technology, with industry expertise in healthcare and insurance. She has significant architecture and management experience in delivering large-scale programs across various industries and platforms.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.