Google Search Feature with Document AI Simplifies Document Extraction!
3025
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
Google Cloud introduced Document AI to automate document processing and to streamline workflows with state-of-the-art machine learning models. With the deep neural networks, the models generalize the learning from seeing hundreds of thousands variations of the documents. But when information is missing or ambiguous on a document – like a missing address or entity name – a human may need to search for it…often on Google.
With Document AI, we are bringing the power of this “Google search” to help customers understand their documents. This means that the same Google knowledge graph technology that helps you find the name, address or phone number of your favorite restaurant can now enrich your document extraction with the right name, fully qualified address, and updated phone number.
Here is a sample payslip…
Imagine a bank employee entering this to capture a customer’s income to qualify them for a loan. When extracting information from this payslip, what employer name should she key in? She might take the time to go into Google and find the right correct legal entity name; or she might just guess and move on, potentially creating data reconciliation headaches down the line.
With Document AI, there is a better way. Our native integration with the knowledge graph means that we can deliver both the specific text from the payslip as well as Google’s best understanding of the actual name of the company that operates at this address. This is an important step to translate from “what has been said” on a document to “what does it mean”. By normalizing the value as you process millions of documents, you are improving accuracy and consistency at the beginning of the data processing workflow, making downstream integration, data analytics and business intelligence tasks at ease.
How EKG Enrichment Works
In a nutshell, Knowledge Graph is a knowledge base that uses a graph data model to integrate interlinked entities, including objects, events, processes or abstracted concepts. Google announced its Knowledge Graph in 2012 as a way to organize information from the Web and to enhance Search results. Different from Google Knowledge Graph, Cloud EKG (Enterprise Knowledge Graph) focuses on entities that are more relevant to enterprise customers, such as organization, product, people, locations, etc.
In EKG, every node is called an entity. Each entity in the graph represents an object, such as an organization. EKG aggregates all the information about a thing into a single entity, thus each entity represents a distinct and identifiable real world concept. The uniqueness of these entities in the graph is one of the reasons that make EKG useful. The edges between nodes are called relationships. When representing attributes, the relationships can be considered as properties, such as the name of a company, the price of a product, etc. When representing relationships, they connect entities in the graph, such as the CEO of a company, the seller of a product.
Entity linking, as its name suggests, is the task of assigning a unique identity to entities mentioned in text, images or videos. In the context of EKG, it connects the text mentions to entities in the graph. Under the hood, the recognition takes both the mention and its context information into consideration for linking to the best matching candidates in the graph.
Entity Enrichment, is essentially linking entities in EKG to documents, and using the attributes of linked entities to enrich the extracted information. The entity linking on documents is based on the understanding of both documents and the entities in the graph. First the document parser annotates entity mentions, which provides semantic meanings to the content of the document. Then Entity Linking selects a list of entities from EKG based on the types of these mentions, and matches to the best entity by comparing the attributes found in the document with the the relationships of the selected entities.
How to leverage the enrichment result
Knowledge graph enrichment is a built-in feature for Lending DocAI , Procurement DocAI and Contract DocAI today, and we are actively working on expanding it to cover more document types and parsers on the platform. To use the knowledge graph enriched values, look out for the entities fields under normalizedValue, returned by the API.
{entities: [{"textAnchor": {"content": "Google Singapore"},…."normalizedValue": {"text": "Google Asia Pacific, Singapore"}}]}
To learn more, check out the Document AI webpage, and EKG Enrichment page to see the list of supported parsers and fields.
3194
Of your peers have already watched this video.
27:30 Minutes
The most insightful time you'll spend today!
Generating Value with AI
Hear how enterprises leveraging the Google Cloud–across industries–are using AI to navigate uncertain times, and how they are innovating with AI to generate value moving forward.
In this video, you’ll uncover how to start using innovations from Google Cloud AI in your business today, and how customers deploy AI to transform their organizations.
ML Workflow Made Simple: How to Automate ML Experiment Tracking with Vertex AI Experiments Autologging
1272
Of your peers have already read this article.
3:30 Minutes
The most insightful time you'll spend today!
Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance.
But tracking experiments is challenging: you need to organize experiments so that other team members can quickly understand, reproduce and compare them. That adds overhead that you don’t need.
We are happy to announce Vertex AI Experiments autologging, a solution which provides automated experiment tracking for your models, which streamlines your ML experimentation
With Vertex AI Experiments autologging, you can now log parameters, performance metrics and lineage artifacts by adding one line of code to your training script without needing to explicitly call any other logging methods.
How to use Vertex AI autologging
As a data scientist or ML practitioner, you conduct your experiment in a notebook environment such as Colab or Vertex AI Workbench. To enable Vertex AI Experiments autologging, you call aiplatform.autolog() in your Vertex AI Experiment session. After that call, any parameters, metrics and artifacts associated with model training are automatically logged and then accessible within the Vertex AI Experiment console.
Here’s how to enable autologging in your training session with a Scikit-learn model.
# Enable autologging
aiplatform.autolog()
# Build training pipeline
ml_pipeline = Pipeline(...)
# Train model
ml_pipeline.fit(x_train, y_train)This video shows parameters and training/post-training metrics in the Vertex AI Experiment console.
Vertex AI SDK autologging uses MLFlow’s autologging in its implementation and it supports several frameworks including XGBoost, Keras and Pytorch Lighting. See documentation for all supported frameworks.
Vertex AI Experiments autologging automatically logs model time series metrics when you train models along multiple epochs. That’s because of the integration between Vertex AI Experiments autologging and Vertex AI Tensorboard.
Furthermore, you can adapt Vertex AI Experiments autologging to your needs. For example, let’s say your team has a specific experiment naming convention. By default, Vertex AI Experiments autologging automatically creates Experiment Runs for you without requiring you to call `aiplatform.start_run()` or `aiplatform.end_run()`. If you’d like to specify your own Experiment Run names for autologging, you can manually initialize a specific run within the experiment using aiplatform.start_run() and aiplatform.end_run() after autologging has been enabled.
What’s next
You can access Vertex AI Experiments autologging with the latest version of Vertex AI SDK for Python. To learn more, check out these resources :
- Documentation: Autolog data to an experiment run
- Github: Get started with Vertex AI Experiments autologging
While I’m thinking about the next blog post, let me know if there is Vertex AI content you’d like to see on Linkedin or Twitter.
1302
Of your peers have already downloaded this article
4:30 Minutes
The most insightful time you'll spend today!
Google Cloud’s managed database services can help you innovate faster and reduce operational overhead. The migration tools and resources included in this whitepaper will help you plan your migration.
This whitepaper provides guidance on:
- Managing services for maximum compatibility with your workloads
- Leveraging services that are compatible with the most popular commercial and open source engines, such as MySQL, PostgreSQL, and Redis
- Using robust tools and services to make migrations simple, secure, and fast with minimal downtime
- Understanding the value of using Google Cloud’s managed database services
Download the Forrester Study to Explore the Benefits of AI for IT Operations in Cloud Environment
7121
Of your peers have already read this article.
1:30 Minutes
The most insightful time you'll spend today!
Organizations are currently modernizing their businesses in order to meet the increasing complexity of today’s business landscape. In effect, business leaders must evaluate the best way to mitigate the challenges which plague their cloud operations, all while meeting customers’ growing expectations around digital experience (DX) through agility, automation, and proactive incident avoidance.
In this commissioned study, “Modernize With AIOps To Maximize Your Impact”, Forrester Consulting surveyed organizations worldwide to better understand how they’re approaching artificial intelligence for IT operations (AIOps) in their cloud environments, and what kind of benefits they’re seeing.
Within this July 2021 study, you’ll see that AIOps systems and principles are here to help. It covers how AIOps increases efficiency and productivity across day-to-day operations, and how businesses are taking note. In fact, 91% of respondents have implemented AIOps to address at least one cloud operations issue, and expansion is set to skyrocket. Those that wait to act, risk losing out on the efficacy of their cloud investment and falling behind their more efficient competitors.
As you can see in the image above, there is a plethora of great information in this complimentary study. So, if you’re looking to enhance your cloud operations and/or adopt AIOps within your organization, be sure to download this free study today.
Revolutionizing Cloud Computing: Introducing G2 VMs with NVIDIA L4 GPUs
1573
Of your peers have already read this article.
2:30 Minutes
The most insightful time you'll spend today!
Organizations across industries are looking to AI to turn troves of data into intelligence, powered by the latest advances in generative AI. Yet for many organizations, there is a barrier to adopting the latest models because they can be costly to train or serve. A new class of cloud GPUs is needed to lower the cost of entry for businesses that want to tap the power of AI.
Today, we’re introducing G2, the newest addition to the Compute Engine GPU family in Google Cloud. G2 is the industry’s first cloud VM powered by the newly announced NVIDIA L4 Tensor Core GPU, and is purpose-built for large inference AI workloads like generative AI. G2 delivers cutting-edge performance-per-dollar for AI inference workloads that run on GPUs in the cloud. By switching from NVIDIA A10G GPUs to G2 instances with L4 GPUs, organizations can lower their production infrastructure costs up to 40%. We also found that customers switching from NVIDIA T4 GPUs to L4 GPUs can achieve 2x-4x better performance. As a universal GPU offering, G2 instances also help accelerate other workloads, offering significant performance improvements on HPC, graphics, and video transcoding. Currently in private preview, G2 VMs are both powerful and flexible, and scale easily from one up to eight GPUs.
Currently organizations require end-to-end enterprise ready infrastructure that will future proof their AI and HPC initiatives for a new era. G2s will be ready to be deployed on Vertex AI, GKE, and GCE, giving customers the freedom to architect their own custom software stack to meet their performance requirements and budget. With optimized Vertex AI support for G2 VMs, AI users can tap the latest generative AI models and technologies. With an easy to use UI and automated workflows, customers can access, tune and serve modern models for video, text, images, and audio without the toil of manual optimizations. The combination of these services with the power of G2 will help customers harness the power of complex machine models for their business.
NVIDIA L4 GPUs with Ada Lovelace Architecture
G2 machine families enable machine learning customers to run their production infrastructure in the cloud for a variety of applications such as language models, image classification, object detection, automated speech recognition, and language translation. Built on the Ada Lovelace architecture with fourth-generation Tensor Cores, the NVIDIA L4 GPU provides up to 30 TFLOPS of performance for FP32, and 242 TFLOPs for FP16. Newly added FP8 support, on top of existing INT8, BFLOAT16 and TF32 capabilities, makes the L4 ideal for ML inference.
With the latest third-generation RT Cores and DLSS 3.0 technology, G2 instances are also great for graphics-intensive workloads such as rendering and remote workstations when paired with NVIDIA RTX Virtual Workstation. NVIDIA L4 provides 3x video encoding and decoding performance, and adds new AV1 hardware-encoding capabilities. For example, G2 can enable gaming customers running game engines such as Unreal and Unity with modern graphics cards to run real-time applications. Likewise, media and entertainment customers that need GPU-enabled virtual workstations can use the L4 to create photo-realistic, high-resolution 3D content for movies, games, and AR/VR experiences using applications such as Autodesk Maya or 3D Studio Max.
What customers are saying
A handful of early customers have been testing G2 and have seen great results in real-world applications. Here are what some of them have to say about the benefits that G2 with NVIDIA L4 GPUs bring:
AppLovin
AppLovin enables developers and marketers to grow with market leading technologies. Businesses rely on AppLovin to solve their mission-critical functions with a powerful, full stack solution including user acquisition, retention, monetization and measurement.
“AppLovin serves billions of AI powered recommendations per day, so scalability and value are essential to our business,” said Omer Hasan, Vice President, Operations at AppLovin. “With Google Cloud’s G2 we’re seeing that NVIDIA L4 GPUs offer a significant increase in the scalability of our business, giving us the power to grow faster than ever before.”
WOMBO
WOMBO aims to unleash everyone’s creativity through the magic of AI, transforming the way content is created, consumed, and distributed.
“WOMBO relies upon the latest AI technology for people to create immersive digital artwork from users’ prompts, letting them create high-quality, realistic art in any style with just an idea,” said Ben-Zion Benkhin, Co-Founder and CEO of WOMBO. “Google Cloud’s G2 instances powered by NVIDIA’s L4 GPUs will enable us to offer a better, more efficient image-generation experience for users seeking to create and share unique artwork.”
Descript
Descript’s AI-powered features and intuitive interface fuel YouTube and TikTok channels, top podcasts, and businesses using video for marketing, sales, and internal training and collaboration. Descript aims to make video a staple of every communicator’s toolkit, alongside docs and slides.
“G2 with L4’s AI Video capabilities allow us to deploy new features augmented by natural-language processing and generative AI to create studio-quality media with excellent performance and energy efficiency” said Kundan Kumar, Head of Artificial Intelligence at Descript.
Workspot
Workspot believes that the software-as-a-service (SaaS) model is the most secure, accessible and cost-effective way to deliver an enterprise desktop and should be central to accelerating the digital transformation of the modern enterprise.
“The Workspot team looks forward to continuing to evolve our partnership with Google Cloud and NVIDIA. Our customers have been seeing incredible performance leveraging NVIDIA’s T4 GPUs. The new G2 instances with L4 GPUS through Workspot’s remote Cloud PC workstations provide 2x and higher frame rates at 1280×711 and higher resolutions” said Jimmy Chang, Chief Product Officer at Workspot.
Pricing and availability
G2 instances are currently in private preview in the following regions: us-central1, asia-southeast1 and europe-west4. Submit your request here to join the private preview, or to receive a notification as when the public preview begins. Support will be coming to Google Kubernetes Engine (GKE), Vertex AI, and other Google Cloud services as well. We’ll share G2 public availability and pricing information later in the year.
More Relevant Stories for Your Company
How Google Cloud Helps SAP Admins Create Scalable, Secure Networks
SAP forms the critical backbone of thousands of enterprises, supporting critical business functions such as finance, supply chain, warehouse management, and more. Google Cloud provides a highly scalable and resilient infrastructure to run such workloads and offers tools, such as Smart Analytics and Machine Learning that can accelerate your organization’s digital transformation. In fact, a
How Apna is using data and AI to drive the gig economy in India
In this episode, Theo speaks to Ronak Shah, Head of Data at apna.co. As one of the largest gig economies in the world, India is seeing rising numbers among younger people joining the ecosystem. Driven by digital adoption and new ways of working, Apna saw an opportunity to help bridge
Google is a Leader in the 2023 Gartner® Magic Quadrant™ for Enterprise Conversational AI Platforms
We’re excited to share that Gartner has recognized Google as a Leader in the 2023 Gartner® Magic Quadrant™ for Enterprise Conversational AI Platforms, authored by Bern Elliot and Gabriele Rigon. We believe this recognition is a testament to Google Cloud’s robust investments and commitment to innovation in AI, coupled with
Empowering AI Startups: Google Cloud’s Game-Changing Benefits
New AI startup program benefits and Accelerator introduced at the Google Cloud Startup Summit Google Cloud is committed to supporting the growth and advancement of startups, with particular focus on helping startups looking to build and scale. We’re seeing tremendous innovation from startups choosing Google Cloud to advance their generative
