Maximize Efficiency in Document Extraction Models with Document AI Workbench GA Release
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Each day, more documents are created and used across companies to make decisions. However, the value in these documents is primarily expressed as unstructured data, which makes the value difficult and manually intensive to extract and use for business processes.
As the number and variety of documents used by businesses proliferate, machine learning (ML) solutions need to be more flexible to handle the broader set of use cases. That’s why we introduced the first Document AI Workbench model, Custom Document Extractor (CDE), in Public Preview at Google Cloud Next ‘22. CDE makes it fast and easy to apply ML to virtually any document-based workflow to extract structured data from unstructured document types, to automate business processes.
CDE lets developers and analysts use their own data to train models and extract fields from documents needed for the business. CDE lets organizations build models faster and with less data — thus accelerating time-to-value for processing and analysis of data in documents.
Today, we announce that Document AI Workbench is Generally Available (GA), open to all customers, ready for production use through APIs and the Google Cloud Console. Document AI Workbench is covered by the Document AI SLA — online and batch document prediction is supported with >=99.9% uptime. Furthermore, Document AI Workbench is now covered by Google Cloud’s GA product terms. For example, Google will notify customers at least 12 months before significantly modifying a customer-facing Google API in a backwards-incompatible manner.
In this blog post, we’ll explore ways customers are already using CDE and Document AI Workbench’s updated capabilities.
What users are saying about Workbench
Deliver higher model accuracy with Workbench
Users leverage Workbench to ultimately save time and money. A third party evaluated Document AI Workbench and concluded that it extracts data more accurately1 than several competing products for document types with variable layouts (e.g. invoice, receipt, bank statements, paystubs). Better accuracy drives higher automation rates, helping Workbench users save time and money.
Chris Jangareddy, managing director for Artificial Intelligence & Data at Deloitte Consulting LLP said, “Google Cloud Document AI is a leading document processing solution packed with rich features like multi-step classify and text extraction to automate sorting, classification, extraction, and quality assurance. By combining Document AI with Workbench, Google Cloud has created a forward-thinking and powerful AI platform for intelligent document processing that will allow for process transformation at an enterprise scale with predictable outcomes that can benefit businesses.”
Mansoor Khan, CEO of OneClinic said, “We help medical professionals scale their clinics through automation. We used Google’s Document AI Workbench to create a model to automatically extract data from patients’ insurance cards as part of our patient check-in software. Workbench is easy to use and we are really happy with the model accuracy — it extracts data more accurately than what we would expect from human data entry.”
Rajnish Palande, VP, Google Business Unit for BFSI, TCS said, “The Google Cloud Document AI Workbench leverages artificial intelligence (AI) to manage and glean insights from unstructured data. The Workbench brings together the power of classification, auto-annotation, page-number identification and multi-language support to help organizations rapidly deliver enhanced accuracy, improved operational efficiency, higher confidence in the information extract, and increased return on investment.”
Build production ready models faster with Workbench
Document AI Workbench helps users create machine learning models faster. For example, a third-party evaluation shows Document AI Workbench trains machine learning models up to 3x faster than a leading competitor. This is an important improvement which lowers total cost of ownership and increases value.
Dallas Dolen, Partner, Google Alliance Leader at PwC said, “Google Document AI Workbench helps to accelerate our custom parser models training as well as improves accuracy and performance using a custom document extractor with human in the loop. It helps us solve complex business problems for our clients in the financial services and healthcare industries.”
Ziang Jia, Senior DocAl Development Lead at Resultant, said, “Document AI Workbench has unlocked a brand-new machine learning development experience for information extraction solutions. Its simplicity and robustness enabled us to build models and deliver a highly accurate outcome in an agile way for a large government agency. We couldn’t be more impressed by its simplicity and robustness and are excited to see how the product will evolve in the future.”
Sean Earley, VP of Delivery Services of Zencore said, “Document AI Workbench allows us to develop highly accurate document parsing models in a matter of days. Our customers have automated tasks that formerly required significant human labor. For example, using Document AI Workbench, a team of two trained a model to split, classify and extract data from 15 document types to automate Home Mortgage Disclosure Act reporting. The mean trained model accuracy was 94%, drastically reducing the operational cost of our customer’s compliance reporting procedures.”
What’s new with Document AI Workbench
The latest Workbench capabilities make it even easier to train and deploy an extraction model:
- With Workbench’s public APIs, you can programmatically create, delete, train, evaluate and deploy models.
- Our updated dataset management tools automatically detect and create existing schema labels from your pre-annotated documents. They also provide you more flexibility when creating and managing schema.
- Our new DocAI Toolkit includes a labeled document converter so that you can easily convert your labeled documents to DocAI’s format and start training faster.
- We’ve reduced the cognitive load for labelers with efficiency enhancements to our Labeling UI.
- The revamped Processor Gallery helps you quickly identify the best model for your use case.
What’s next for Document AI Workbench
We continue to invest in Document AI Workbench to help you automate document processing. Here are a few things we’re working on that we’re excited about:
- Classify document types with the Custom Document Classifier (CDC), coming soon in public preview
- Copy processor versions across projects and processors to streamline managing development and production environments
- Support larger documents (e.g., longer than 50 pages) so you can process a wider array of documents
- Broader (non-latin) language support–equivalent to Document AI OCR
- And many more investments, using state of the art technology, to help you build world class models faster to automate document processing
Document AI Workbench is in GA and ready for production workloads. Learn more via Document AI Workbench documentation or try it out in the Google Cloud Console.
Acknowledgements: Tomas Moreno, Outbound Product Manager, Lukas Rutishauser, Software Engineering Manager, Michael Kwong, Software Engineering Manager, Rajagopal Janani, Software Engineering Manager, Michael Lanning, UX Designer.
1. When trained with 200+ documents
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Public cloud, big data, and AI technologies offer competitive advantages and cost savings for capital markets firms ready to make the transition. This paper discusses the three phases capital markets firms go through in transitioning to public cloud, and the workloads, benefits, and cultural changes that characterize the three phases:
Infrastructure Optimizers: The first step on the public cloud journey, where firms focus on migrating specific workloads to save costs.
Cautious Strategists: Firms build on the success of their first public cloud migrations, and begin to change the way they develop technology to increase cost savings and start taking advantage of capabilities only available on public cloud.
Transformative Innovators: Firms shift to a fully public cloud-enabled mentality, and fully leverage the flexibility and agility of the public cloud to build industry-changing solutions and attract top IT talent.
Additionally, we reveal the five things that capital markets innovators who have advanced to the transformation phase do well in their adoption of cloud, big data, and AI technologies across the front, middle, and back office functions.
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Chronicle Security Analytics: Key to Address Security Data Overload
Google Cloud’s Chronicle is a security analytics platform built for modern use cases to combat modern threats. In today’s world, enterprises have undergone significant changes in all aspects, and must adapt to their security needs to counter threats and attacks. Watch the video to learn Google Cloud’s initiative to help businesses improve their security by 10x to catch up with the way the world is changing and how Chronicle Security Analytics is poised to help address data security challenges.
Looking for a Cloud Data Warehouse? Find out Why Forrester Thinks Google BigQuery is a Leader
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We are thrilled to announce that Google has been named a Leader in The Forrester Wave™: Cloud Data Warehouse, Q1 2021 report. For more than a decade, BigQuery, our petabyte-scale cloud data warehouse, has been in a class of its own. We’re excited to share this recognition and we want to thank our strong community of customers and partners for voicing their opinion. We believe this report validates the alignment of our strategy with our customers’ analytics needs.
“Customers like Google’s frequency of data warehouse releases, business value, future proof architecture, high-end scale, geospatial capabilities, strong AI/ML capabilities, good security capabilities, and broad analytical use cases,” according to the Forrester report. Today’s data leaders require a data warehousing platform that provides both depth and breadth and with BigQuery, organizations are able to unlock deeper data science and machine learning capabilities while promoting data democratization and providing the highest levels of availability.
Google BigQuery: 5 out of 5!
Forrester gave Google BigQuery a score of 5 out of 5 across 19 different criteria, including:
Today, customers across the globe use BigQuery to run business critical analytics workloads to enable BI acceleration, IoT analytics, customer intelligence, AI/ML-based analytics, data science, data collaboration, and data services. Customers such as Verizon, Wayfair, HSBC, Twitter, AirAsia, KeyBank, The Home Depot, and Vodafone have anchored their digital transformation efforts on BigQuery—unlocking deeper insights for their people.
Customers use BigQuery across all industries, to solve issues like credit card fraud detection, predictive forecasting, anomaly detection, log analytics and many more. Forrester recognized this work and gave BigQuery a 5/5 score for supporting vertical and horizontal use cases.
More BigQuery advantages
Google is the first hyperscale provider to offer a multi cloud data analytics solution. With BigQuery Omni, customers can perform cross-cloud analytics with ease, and drive business outcomes they couldn’t achieve with the siloed approach offered by other vendors.
We designed BigQuery to be highly scalable and more open and interoperable so that customers can join data across SQL databases, traditional unstructured data lakes in object storage, and even spreadsheets using any analysis tool.
Recent innovations like BigQuery BI Engine lets us provide the best analytics experience by delivering sub-second query response times from any business intelligence tool, from Google’s Looker and Connected Sheets to Tableau, Microsoft Power BI, ThoughtSpot, and others. Our goal is to meet customers where they are rather than force them into a one-size-fits-all approach to data analysis.
With BigQuery, organizations gain both breadth and depth of capabilities to transform their analytics strategy. Forrester also gave BigQuery 5 out of 5 in:
Data-powered innovation
These advantages enable our customers to accelerate their digital transformation and reimagine their business through data-powered innovation. Google’s leadership across AI, analytics, and databases comes together in a single data cloud platform that provides everyone with the ability to get value out of their data faster.
We bring decades of research and innovation in AI to our customers through industry-leading AI solutions, that in turn helps our customers solve their biggest problems. Our support of open standards and APIs enables interoperability between a variety of services for ingestion, storage, processing and analytics across the data cloud platform. And finally, we believe our multi-layered security approach throughout the data stack ensures redundancy and reliability so that customers can have the peace of mind that their data is always protected.
We are honored to be a leader in this Forrester Wave™ and look forward to continuing to innovate and partner with you on your digital transformation journey.
Download the full Forrester Wave™ :Cloud Data Warehouse, Q1 2021 report. And check out these smart analytics reference patterns. To learn more about BigQuery, visit our website, and get started immediately with the free BigQuery Sandbox.
Advance Your Cloud Career: Seven New No-Cost Generative AI Training Courses
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Editor’s note: This blog has been updated since it originally published to reflect the total number of generative AI courses that have become available since the publish date.
An AI/ML (artificial intelligence/machine learning) career path can be a great specialty area within the cloud—and one of the most accessible! Because this area is constantly developing, most recently with the rise of generative AI, I want to share some recommendations to help you chart a sustainable career as AI/ML continues to evolve.
Generative AI falls under the overall category of AI and offers a new and exciting way of interacting with information, brands, and other people. Let’s talk about how these disciplines work together and about materials available to help you upskill in these areas.
To this end, we are happy to announce a new set of generative AI training content available at no cost. So, whether you are just getting started or already have a more advanced role, read on to find ways to help reach your desired position.
ML compared to AI
While the terms ML and AI are often used interchangeably, there are distinct differences. AI is an umbrella concept wherein machines are taught to perform tasks normally associated with human intelligence, such as decision-making and language interaction. ML is a subset of AI dedicated to taking data from the past and training algorithms to create models that can perform highly complex tasks without being explicitly programmed. It’s the basis for most forms of AI that people interact with, like virtual assistants, music recommendations, and chatbots.
Data engineers and ML engineers work with data scientists to get insights from data. They are needed to create software models and get clear results, and develop deployable applications. These skilled roles are needed in every industry!
Change the world for the better with generative AI
I recently wrote about four key pillars of technology trends expected in the next decade, including the role of AI/ML in the cloud environment as one of those pillars, and how you can bridge the skills gap and build your career.
Generative AI is a new type of ML that has made a lot of headlines recently. Research from CIO Dive finds that seven in 10 executives say their companies are investigating or exploring generative AI. Now is a great time to become an expert, while we are at the cusp of this technology becoming more widely adopted.
To get to generative AI, we need to talk first about deep learning. Deep learning is a subset of ML that uses artificial neural networks to process more complex patterns than traditional ML. Generative AI sits still further down the funnel, as a subset of deep learning that typically involves the Transformer architecture. Essentially, it’s a type of AI that can map long-range dependencies and patterns in large training sets, then use what it learns to produce new content, including text, imagery, audio, and synthetic data.
Generative AI relies on large models, such as large language models (LLMs) that can classify and generate text, answer questions, and summarize documents. For more detail about how this works, check out the video below from Google I/O 2023, which features great information from Dr. Gwendolyn Stripling, Artificial Intelligence Technical Curriculum Developer for Google Cloud.
How Google is offering generative AI
Here at Google, we’ve been heavily invested for decades in research and innovation aimed at helping businesses, governments, and developers maximize the potential of AI. Our vision is to empower builders, innovators, developers, and doers to use AI in unique, responsible, productive ways.
To deliver on this, we recently announced a variety of solutions to bring generative AI into our offerings, beginning with Generative AI support on Vertex AI and Gen App Builder.
Vertex AI is Google Cloud’s ML platform for training and deploying ML models and AI applications. With Generative AI support on Vertex AI, data science teams and developers can access foundation models from both Google and other sources, helping them to quickly build, customize, and deploy models for their own use cases.
As part of generative AI support on Vertex AI, Model Garden and Generative AI Studio are now in preview, including access to PaLM 2 for Text and Chat and Embeddings API for Text, while other features and services are available to select trusted testers. If you’d like early access to Google Cloud’s AI products, join the waitlist here.
We announced our latest PaLM model in production at I/O 2023: PaLM 2. Today, it powers more than 25 products! Additionally, we’ve fine-tuned PaLM 2 for specific use cases, including security and medical domains. You’ve probably also heard of Bard, our experiment for conversational AI, which is fully running on PaLM 2.
Our Gen App Builder aims to make customer, partner, and employee interactions more effective and helpful. With it, developers — even those with limited data science expertise — can quickly create bots, chat apps, digital assistants, custom search engines, and more. Gen App Builder even makes it possible to create some generative AI apps without any coding skills.
Now, let’s bring these products to life! Not sure exactly the use case or in need of a little inspiration? Check out this blog from Google Cloud CEO Thomas Kurian to see how customers and partners in the ecosystem are bringing ideas to life with generative AI. And watch this video to see generative AI in action!
Build your AI/ML skills and validate your knowledge to grow your career
There is a lot of excitement around generative AI—it’s truly a brand new path to consider for your AI/ML career! Check out my recommendations below for training options for AI/ML roles. These will help you gain critical skills as generative AI becomes more widely available.
*NEW* training materials, specific to generative AI technologies
Generative AI Learning Path – no cost training
- [Course] Introduction to Generative AI (1 day)
- [Course] Introduction to Large Language Models (1 day)
- [Course] Attention Mechanism (1 day)
- [Course] Transformer Models and BERT Model (1 day)
- [Course] Introduction to Image Generation (1 day)
- [Course] Create Image Captioning Models (1 day)
- [Course] Encoder-Decoder Architecture (1 day)
Additional AI/ML training – varying learning credits required to complete on Google Cloud Skills Boost
Introductory level
- [Course with completion badge] How Google Does Machine Learning (1 day)
- [Course with completion badge] MLOps: Getting Started (1 day)
- [Skill badge] Get started with TensorFlow on Google Cloud (8 hours)
- [Skill badge] Perform foundational ML, AI and data tasks in Google Cloud (7 hours)
- [Course with completion badge] Language, Speech, Text, and Translation with Google Cloud APIs (5 hours)
Intermediate/Multi-level
- [Learning Path] Machine Learning Engineer (Collection of 15 video courses and labs)
- [Skill badge] Build and Deploy Machine Learning Solutions on Vertex AI (1 day)
Advanced
- [Training + certification exam] Google Cloud Professional ML Engineer Certification (varied time)
- [Skill badge] Machine Learning with TensorFlow in Vertex AI (90 minutes)
- [Course with completion badge] Natural Language Processing in Google Cloud (1 day)
You can also catch up on hands-on demos in our most recent Cloud OnBoard: From Data to AI with BigQuery and Vertex AI. In it, we guide you through all steps of the data-to-AI solution.
Coming up on June 29, we are hosting Getting Started with Vertex Generative AI as part of Innovators Live. Register now!
I also encourage you to consider an Innovators Plus subscription. You get access to over 700 labs, skill badges and courses (including many that are specific to AI/ML), a certification exam voucher, and up to $1,000 in Google Cloud credits. You’ll also get access to live learning events and technical briefings with Google Cloud experts, plus 1:1 consultations (talk with us about your upcoming generative AI project!). It’s a $1,500 package for only $299. Learn more!
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To make great products: do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.
Most of the problems you will face are, in fact, engineering problems. Even with all the resources of a great machine learning expert, most of the gains come from great features, not great machine learning algorithms. So, the basic approach is:
- Make sure your pipeline is solid end to end.
- Start with a reasonable objective.
- Add common-sense features in a simple way.
- Make sure that your pipeline stays solid.
This approach will work well for a long period of time. Diverge from this approach only when there are no more simple tricks to get you any farther. Adding complexity slows future releases.
Once you’ve exhausted the simple tricks, cutting-edge machine learning might indeed be in your future. See the section on Phase III machine learning projects.
This document is arranged as follows:
- The first part should help you understand whether the time is right for building a machine learning system.
- The second part is about deploying your first pipeline.
- The third part is about launching and iterating while adding new features to your pipeline, how to evaluate models and training-serving skew.
- The final part is about what to do when you reach a plateau.
- Afterwards, there is a list of related work and an appendix with some background on the systems commonly used as examples in this document.
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