Raj Pathak

Intelligent document processing (IDP) with AWS helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills. Faster information extraction with high accuracy can help you make quality business decisions on time, while reducing overall costs. For more information, refer to Intelligent document processing with AWS AI services: Part 1.

However, complexity arises when implementing… Show more

Intelligent document processing (IDP) with AWS helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills. Faster information extraction with high accuracy can help you make quality business decisions on time, while reducing overall costs. For more information, refer to Intelligent document processing with AWS AI services: Part 1.

However, complexity arises when implementing real-world scenarios. Documents are often sent out of order, or they may be sent as a combined package with multiple form types. Orchestration pipelines need to be created to introduce business logic, and also account for different processing techniques depending on the type of form inputted. These challenges are only magnified as teams deal with large document volumes.

In this post, we demonstrate how to solve these challenges using Amazon Textract IDP CDK Constructs, a set of pre-built IDP constructs, to accelerate the development of real-world document processing pipelines. For our use case, we process an insurance document to enable straight-through processing, but you can extend this solution to any use case, which we discuss later in the post. Show less

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale.

Many of the RStudio on SageMaker users are also users of Amazon Redshift, a fully… Show more

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry’s first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale.

Many of the RStudio on SageMaker users are also users of Amazon Redshift, a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Users can also interact with data with ODBC, JDBC, or the Amazon Redshift Data API.

The use of RStudio on SageMaker and Amazon Redshift can be helpful for efficiently performing analysis on large data sets in the cloud. However, working with data in the cloud can present challenges, such as the need to remove organizational data silos, maintain security and compliance, and reduce complexity by standardizing tooling. AWS offers tools such as RStudio on SageMaker and Amazon Redshift to help tackle these challenges.

In this blog post, we will show you how to use both of these services together to efficiently perform analysis on massive data sets in the cloud while addressing the challenges mentioned above. This blog focuses on the Rstudio on Amazon SageMaker language, with business analysts, data engineers, data scientists, and all developers that use the R Language and Amazon Redshift, as the target audience. Show less

In a previous post titled “Engineering for DEI: Tapping IT Creativity and Technique to Address Diversity, Equity, and Inclusion,” authors Wesley Story and Mark Schwartz recommend rethinking organizational approaches to delivering DEI outcomes. They challenge readers to approach DEI like core engineering projects.

Shifting from monolithic develop-then-test approaches to a more iterative mindset creates the space for engineers to enhance minimum viable products with the aim of identifying… Show more

In a previous post titled “Engineering for DEI: Tapping IT Creativity and Technique to Address Diversity, Equity, and Inclusion,” authors Wesley Story and Mark Schwartz recommend rethinking organizational approaches to delivering DEI outcomes. They challenge readers to approach DEI like core engineering projects.

Shifting from monolithic develop-then-test approaches to a more iterative mindset creates the space for engineers to enhance minimum viable products with the aim of identifying and correcting deficiencies.

“If your company has been unsuccessful in meeting its DEI objectives, consider that a defect. As with any defect, you have to use your problem-solving skills to fix it, then redeploy. Create an acceptance test for meeting your diversity goals. Keep working until the test passes.” Show less

Amazon Comprehend customers can now extract the sentiments associated with entities from text documents in real-time using the newly released synchronous API. Targeted Sentiment synchronous API enables customers to derive granular sentiments associated with specific entities of interest such as brands or products without waiting for batch processing.

With ever-increasing data volume being generated, datasets used for ML and statistical analysis are growing in tandem. With this brings the challenges of increased development time and compute infrastructure management. To solve these challenges, data scientists have looked to implement parallel data processing techniques. Parallel data processing, or data parallelization, takes large existing datasets and distributes them across multiple processers or nodes to operate on the data… Show more

With ever-increasing data volume being generated, datasets used for ML and statistical analysis are growing in tandem. With this brings the challenges of increased development time and compute infrastructure management. To solve these challenges, data scientists have looked to implement parallel data processing techniques. Parallel data processing, or data parallelization, takes large existing datasets and distributes them across multiple processers or nodes to operate on the data simultaneously. This can allow for faster processing time of larger datasets, along with optimized usage on compute. This can help ML practitioners create reusable patterns for dataset generation, and also help reduce compute infrastructure load and cost. Show less

Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus on multiple dimensions of diversity are 45% more likely to grow their market share and 70% more… Show more

Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus on multiple dimensions of diversity are 45% more likely to grow their market share and 70% more likely to capture new markets.

DEI initiatives can be difficult and complex to scale, taking long periods of time to show impact. As such, organizations should plan initiatives in phases, similar to an agile delivery process. Achieving small but meaningful wins at each phase can contribute towards larger organizational goals. An example of such an initiative at Amazon is the “Say my Name” tool.

Amazon’s global workforce—with offices in over 30 countries—requires the consistent innovation of inclusive tools to foster an environment that dispels unconscious bias. “Say my Name” was created to help internal Amazon employees share the correct pronunciation of their names and practice saying the name of their colleagues in a culturally competent manner. Incorrect name pronunciation can alienate team members and can have adverse effects on performance and team morale. A study by Catalyst.org reported that employees are more innovative when they feel more included. In India, 62% of innovation is driven by employee perceptions of inclusion. Adding this pronunciation guide to written names aims to create a more inclusive and respectful professional environment for employees. Show less

Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus on multiple dimensions of diversity are 45% more likely to grow their market share and 70% more… Show more

Organizational diversity, equity and inclusion (DEI) initiatives are at the forefront of companies across the globe. By constructing inclusive spaces with individuals from diverse backgrounds and experiences, businesses can better represent our mutual societal needs and deliver on objectives. In the article How Diversity Can Drive Innovation, Harvard Business Review states that companies that focus on multiple dimensions of diversity are 45% more likely to grow their market share and 70% more likely to capture new markets.

DEI initiatives can be difficult and complex to scale, taking long periods of time to show impact. As such, organizations should plan initiatives in phases, similar to an agile delivery process. Achieving small but meaningful wins at each phase can contribute towards larger organizational goals. An example of such an initiative at Amazon is the “Say my Name” tool.

Amazon’s global workforce—with offices in over 30 countries—requires the consistent innovation of inclusive tools to foster an environment that dispels unconscious bias. “Say my Name” was created to help internal Amazon employees share the correct pronunciation of their names and practice saying the name of their colleagues in a culturally competent manner. Incorrect name pronunciation can alienate team members and can have adverse effects on performance and team morale. A study by Catalyst.org reported that employees are more innovative when they feel more included. In India, 62% of innovation is driven by employee perceptions of inclusion. Adding this pronunciation guide to written names aims to create a more inclusive and respectful professional environment for employees. Show less

The sentiment analysis APIs provided by Amazon Comprehend help businesses determine the sentiment of a document. You can gauge the overall sentiment of a document as positive, negative, neutral, or mixed. However, to get the granularity of understanding the sentiment associated with specific products or brands, businesses have had to employ workarounds like chunking the text into logical blocks and inferring the sentiment expressed towards a specific product.

To help simplify this… Show more

The sentiment analysis APIs provided by Amazon Comprehend help businesses determine the sentiment of a document. You can gauge the overall sentiment of a document as positive, negative, neutral, or mixed. However, to get the granularity of understanding the sentiment associated with specific products or brands, businesses have had to employ workarounds like chunking the text into logical blocks and inferring the sentiment expressed towards a specific product.

To help simplify this process, starting today, Amazon Comprehend is launching the Targeted Sentiment feature for sentiment analysis. This provides the capability to identify groups of mentions (co-reference groups) corresponding to a single real-world entity or attribute, provide the sentiment associated with each entity mention, and provide the classification of the real-world entity based on a pre-determined list of entities. Show less

Intelligent document processing (IDP) is a common use case for customers on AWS. You can utilize Amazon Comprehend and Amazon Textract for a variety of use cases ranging from document extraction, data classification, and entity extraction. One specific industry that uses IDP is insurance. They use IDP to automate data extraction for common use cases such as claims intake, policy servicing, quoting, payments, and next best actions. However, in some cases, an office receives a document with… Show more

Intelligent document processing (IDP) is a common use case for customers on AWS. You can utilize Amazon Comprehend and Amazon Textract for a variety of use cases ranging from document extraction, data classification, and entity extraction. One specific industry that uses IDP is insurance. They use IDP to automate data extraction for common use cases such as claims intake, policy servicing, quoting, payments, and next best actions. However, in some cases, an office receives a document with complex, label-less information. This is normally difficult for optical character recognition (OCR) software to capture, and identifying relationships and key entities becomes a challenge. The solution is often requires manual human entry to ensure high accuracy.

In this post, we demonstrate how you can use named entity recognition (NER) for documents in their native formats in Amazon Comprehend to address these challenges. Show less

Speaker for Computer Vision and Natural Language processing workshop for AWS Re:Invent 2021

Mortgage packets have hundreds of documents in various layouts and formats. Using machine learning (ML), you can set up a document-processing pipeline to automate mortgage application workflows like extracting text from W2s, paystubs, and deeds; classifying documents; or using custom entity recognition to pull out specific data points. In this workshop, learn various ways to use optical character… Show more

Speaker for Computer Vision and Natural Language processing workshop for AWS Re:Invent 2021

Mortgage packets have hundreds of documents in various layouts and formats. Using machine learning (ML), you can set up a document-processing pipeline to automate mortgage application workflows like extracting text from W2s, paystubs, and deeds; classifying documents; or using custom entity recognition to pull out specific data points. In this workshop, learn various ways to use optical character recognition (OCR), natural language processing (NLP), and human-in-the-loop services to build a document-processing pipeline to automate mortgage applications—saving time, reducing manual effort, and improving ROI for your organization. Show less

Many organizations spanning different sizes and industry verticals still rely on large volumes of documents to run their day-to-day operations. To solve this business challenge, customers are using intelligent document processing services from AWS such as Amazon Textract and Amazon Comprehend to help with extraction and process automation. Before you can extract text, key-value pairs, tables, and entities, you need to be able to split multipage PDF documents that often contain heterogeneous… Show more

Many organizations spanning different sizes and industry verticals still rely on large volumes of documents to run their day-to-day operations. To solve this business challenge, customers are using intelligent document processing services from AWS such as Amazon Textract and Amazon Comprehend to help with extraction and process automation. Before you can extract text, key-value pairs, tables, and entities, you need to be able to split multipage PDF documents that often contain heterogeneous form types. For example, in mortgage processing, a broker or loan processing individual may need to split a consolidated PDF loan package, containing the mortgage application (Fannie Mae form 1003), W2s, income verification, 1040 tax forms, and more.

To tackle this problem, organizations use rules-based processing: identifying document types via form titles, page numbers, form lengths, and so on. These approaches are error-prone and difficult to scale, especially when the form types may have several variations. Accordingly, these workarounds break down quickly in practice and increase the need for human intervention.

In this post, we show how you can create your own document splitting solution with little code for any set of forms, without building custom rules or processing workflows. Show less

Insurance is a tricky field to change, especially with Farm Mutual Insurers. Your business has been working fine for hundreds of years; why jeopardize a good thing? Because the time is right! In the face of customer demand and planned core application upgrades, this opens an opportunity to meld innovation with traditional business practices to provide substantial cost savings. In this session, you’ll discover how we’re saving our customers over 45,000 hours annually using Documents AI, Google… Show more

Insurance is a tricky field to change, especially with Farm Mutual Insurers. Your business has been working fine for hundreds of years; why jeopardize a good thing? Because the time is right! In the face of customer demand and planned core application upgrades, this opens an opportunity to meld innovation with traditional business practices to provide substantial cost savings. In this session, you’ll discover how we’re saving our customers over 45,000 hours annually using Documents AI, Google Cloud Vision, and other GCP products. We’ll also touch on the challenges of working with a static industry in an era marked by the metamorphosis of both technology and consumer demands, and how to steer your customers towards a future in emerging technology.

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