AI researchers and engineers need better data to enable better AI solutions. The quality of an AI solution is determined by both the learning algorithm (such as a deep-neural network model) and the datasets used to train and evaluate that algorithm. Historically, AI research has focused much more on algorithms than datasets, despite their vital importance. As a result, many algorithms are freely available as starting points, but many important problems lack large, high-quality open datasets. Further, creating new datasets is expensive and error-prone.

Recently, the data-centric AI movement has emerged, which aims to develop new methodologies and tools for constructing better datasets to fix this problem. Conferences, workshops,  challenges, and platforms are being launched to support improving data quality and to foster data excellence. Thought leaders such as Andrew Ng at Landing.AI and Chris Re at Stanford University are encouraging AI developers to focus more on iterative data engineering than they do tuning their learning algorithms. Our CHI-best-paper-award-winning paper, “Everyone wants to do the model work, not the data work” highlighted the significance of data quality in the practice of ML. 

At Google, we are excited to contribute to data-centric AI. Today, Google Cloud is adding a new high value dataset to the Public Dataset Program, and Google researchers are announcing DataPerf, a new multi-organizational effort to develop benchmarks for data quality and data centric algorithms.

Google Cloud is committed to helping users improve their data quality, starting with supporting better public data. The Public Datasets program provides high quality datasets pre-configured on GCP for easy access. Google Cloud is adding a new high-value dataset developed by the MLCommons™ Association (which Google co-founded) to the Public Datasets program: The Multilingual Spoken Words Corpus: a rich audio speech dataset with more than 340,000 keywords in 50 languages with upwards of 23.4 million examples.

This new public dataset is aligned with the MLCommons Association vision for “open” datasets – accessible by all – that are “living” – continually being improved to raise quality and increase representation and diversity.

Google researchers, in collaboration with multiple organizations, are announcing the DataPerf effort at the NeurIPS Data-Centric AI workshop today, to develop benchmarks to improve data quality. Much like the the MLPerf™ benchmarking effort which is now the industry standard for machine learning hardware/software speed, DataPerf brings together the originators of prior efforts including: CATS4ML, Data-Centric AI Competition, DCBench, Dynabench, and the MLPerf benchmarks to define clear metrics that catalyze rapid innovation. DataPerf will measure the utility of training and test data for common problems, and algorithms for working with datasets such as: selecting core sets, correcting errors, identifying under-optimized data slices, and valuing datasets prior to labeling.

Together, supporting open, living datasets for core ML tasks, and the development of benchmarks to direct the rapid evolution of those datasets will empower the researchers and engineers who use Google Cloud to do even more amazing things – and we can’t wait to see what they create!

Acknowledgements: In collaboration with Lora Aroyo and Praveen Paritosh.

The home lending journey entails processing an immense number of documents daily from hundreds of thousands of borrowers. Currently, home lending document processing relies on some outdated digital models and a high dependency on manual labor, resulting in slow processing times and higher origination costs. Scaling a business that sorts through millions of documents daily, while increasing efficacy and accuracy, is no small feat. When it comes to applying for a mortgage loan, consumers expect a digital experience that’s as good as the in-person one. Roostify simplifies the home lending journey for lenders and their customers.

No time to spare: Overcoming document processing challenges with AI

Roostify provides enterprise cloud applications for mortgage and home lenders. In order to empower its customers to deliver a better, more personalized lending experience, they needed to automate and scale their in-house document parsing functionality.

As a key component of its document intelligence service, Roostify is leveraging Google Cloud’s Lending DocAI machine learning platform to automate processing documents required during a home loan application process, such as tax returns or bank statements with multi-language support. This partnership delivers data capture at scale, enabling Roostify customers to automatically identify document types from the uploaded file and to extract relevant entities such as wages, tax liabilities, names, and ID numbers for further processing, and make things move faster in the cumbersome lending process. 

Roostify’s solutions leverage Google Cloud’s Lending DocAI, which is built on the recently announced Document AI platform, a unified console for document processing. Customers can easily create and customize all the specialized parsers (e.g., mortgage lending documents and tax returns parsers) on the platform without the need to perform additional data mapping or training. All Google Cloud’s specialized parsers are fine-tuned to achieve industry-leading accuracy, helping customers and partners confidently unlock insights from documents with machine learning. Learn more about the solution from the GA launch blog and the overview video.

Integrating Lending DocAI’s intelligent document processing capabilities into the Roostify platform means more innovation for their customers and tangible results: faster loan processing times, fewer document intake errors, and lower origination costs. Additional support in Google Lending DAI for other languages and more documents like global Know Your Customer (KYC) documents or payroll reports is in the near future.

Full integration of AI solutions

Working together with Roostify’s platform team, we were able to help them solve their document processing challenge through integration of various GCP products such as Lending DocAI (LDAI), Data Loss Prevention (DLP) for redacting sensitive data, BigQuery for data warehousing and analytics, and Firestore for API status. To make it very safe and secure, all data was encrypted end-to-end at Rest and in Transit. LDAI won’t require any training data to process. It is an easy plug and play API.

Here is a sneak peek in the high level deployment architecture for LDAI in Roostify environment:

Here are the steps for processing data:

1. Receives document processing request from the client.
2. API Function directs requests to the pre-processing service. For Async requests a processing ID is generated and returned to the caller.
3. Pre-processing service sends the request for further processing (Long/short PDF conversion), calling other microservices and receives back the responses. Any error in the response received is then sent to the response processing service. 
4. If the response is synchronous, the pre-processing service directs it to the LDAI Invoker service. 
   1. If the response is asynchronous, the pre-processing service feeds it into the Cloud Pub/Sub service.
5. Cloud Pub/Sub service feeds the response back to the LDAI Invoker service.
6. LDAI Invoker service routes the request to the Google LDAI API for classification if there are multiple pages in the document.
7. Document will be split based on LDAI response and then saved in a GCS bucket for temporary storage.
8. LDAI entity interface for single page processing and then LDAI Invoker sends LDAI results to LDAI Response Processing
9. If a request is a synchronous request the LDAI Response Processor sends results to the API Function so that it can complete the synchronous call and respond to the rConnect caller.
   1. If the request is an asynchronous request the LDAI Response Processor will respond to the caller’s webhook and complete the transaction.
10. Finally, Data stored in the GCP bucket will be deleted.

All the responses that come from the LDAI API can optionally feed into BigQuery via the Response Processor, after parsing it through Data Loss Prevention (DLP) API to redact the PII/sensitive information.  Throughout the processing of both asynchronous and synchronous requests all transactions are logged using Cloud Logging.  For asynchronous transactions, the state is maintained throughout the process using Cloud Firestore.

Roostify currently uses this technology to power two different solutions: Roostify Document Intelligence and Roostify Beyond™. Roostify Document Intelligence is a real-time document capture, classification, and data extraction solution built for home lenders. It ingests documents uploaded by borrowers and loan officers, identifies the relevant documents, and extracts and classifies key information. Roostify Document Intelligence is available as a standalone API service to any home lender with any digital lending infrastructure already in place. 

Roostify Beyond™ is a robust suite of AI-powered solutions that enables home lenders to create intelligent experiences from start to close. It combines powerful data, insightful analytics, and meaningful visualization to streamline the underwriting process. Roostify Beyond™ is currently available only to Roostify customers as part of an Early Adopter program and will be rolled out to the market later this year.

Through this partnership, Roostify has enabled its customers to adopt a data-first approach to their home lending processes, which will lead to improved user experiences and significantly reduced loan processing times.

Fast track end-to-end deployment with Google Cloud AI Services (AIS)

Google AIS (Professional Services Organization), in collaboration with our partner Quantiphi, helped Roostify deploy this system into production and fast-tracked the development multifold to generate the final business value.

The partnership between Google Cloud and Roostify is just one of the latest examples of how we’re providing AI-powered solutions to solve business problems.

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 recent study by Forrester found that running SAP on Google Cloud can generate a 160% return on investment and a payback period of six months or less, thanks to legacy infrastructure cost savings, downtime avoidance, and productivity improvements.

How you deploy your SAP systems across your network has a tremendous impact on its availability, resilience, and performance. In addition to separate production and high-availability (HA) environments, SAP deployments typically include sandbox, development, quality assurance (QA), and disaster recovery environments as well. 

Because most of the Google network is virtual, SAP administrators can easily design complex landscapes that suit your organization’s SAP deployment and organizational structure while also meeting security and operational requirements. 

As you get started with SAP on Google Cloud, you’ll need to decide how to configure your networking to ensure the availability and performance of various SAP systems. Here’s a look at your options.

VPC and shared VPC

A virtual private cloud (VPC) is a secure, isolated private network hosted within Google Cloud. VPCs are global in Google Cloud, so a single VPC can span multiple regions without communicating across the public internet. Similarly, subnets can span across zones within a region. A zone represents a single failure domain, so typical SAP deployments place production and HA systems in different zones to ensure resiliency. Google Cloud simplifies this type of deployment, because subnets containing both production and HA systems can span multiple zones.  

This capability also simplifies SAP clustering, since the cluster’s virtual IP (VIP) address can be in the same range as those of the production and HA machines. This configuration shields the floating IP using Google internal load balancers and is applicable to HA clustering of the application layer (ASCS and ERS) and the HANA database layer (HANA Primary and Secondary).

Shared VPCs are a feature unique to Google Cloud that allows an organization to connect resources from multiple projects to a common VPC network. This lets them communicate with each other securely and efficiently using internal IPs. You can also centrally control the network for all SAP projects (service) from the Host project while using firewalls to inspect communication between compute engines in the same subnet, and between those in different subnets. (Best practice is to limit the communication between these systems to only the required ports — typically via SAP remote function call (RFC) communication at Layer 4.)

When designing your network, start with a host project containing one or more Shared VPC networks. You can attach additional service projects to a host project, which allows them to participate in the Shared VPC. It’s common practice to have multiple service projects operated and administered by various departments or teams in your organization.

Depending on your needs, you can deploy SAP on a single Shared VPC or multiple ones. The two scenarios differ in terms of network control, SAP environment isolation, and network inspection. Let’s look more closely at these differences.

Scenario 1: Deploying SAP on a single Shared VPC

If you require only a single network inspection, deploying SAP on a single Shared VPC has the advantage of simplicity and reduces administrative overhead.

• Network control: The Shared VPC serves as the network hub, allowing central network management based on identity access management (IAM) roles for the network team(s) in both production and non-production environments.
• SAP environment isolation: You can create projects and subnets for each SAP environment. Projects help group resources together for finer IAM control and billing visibility, while subnets provide network isolation for individual SAP environments. In service projects, compute engines can communicate by default; however, you can adopt simple firewall rules to block communication between compute engines within a subnet or in separate subnets.
• Network inspection: Use Google Cloud firewalls to allow only the required ports for communication between SAP systems. Leverage network tags and service accounts to define granular control for both north-south and east-west traffic.

Scenario 2: Multiple Shared VPCs for SAP deployment

In scenarios requiring additional network inspections, you can create multiple Shared VPCs, typically one per environment. Use peering between these Shared VPCs to enable RFC communication among the SAP development, QA, and production systems.

• Network isolation: Multiple Shared VPCs are completely isolated from each other except via specific ports opened in Google Cloud firewalls. This allows additional East-West traffic inspection by a Network Virtual Appliance (NVA) within a Google Cloud network. 
• Network control:As the number of Shared VPCs increases, activities such as peering and firewall policies also increase. This diminishes the central network control that Shared VPCs offer, so the network team should plan to manage the policies in each VPC separately.

Hybrid scenarios – for example, one Shared VPC for the production environment and one Shared VPC for all non-production systems — are also possible. This arrangement allows network inspection between production and non-production systems, and limits the number of central network administration layers to two.

Configuring the networking environment for multiple SAP systems can be a complex process. Thanks to Google Cloud’s Shared Virtual Clouds and other tools, SAP administrators can create scalable, secure networks that provide logic, resilience, and visibility to their cloud deployments.Learn more about these networking capabilities and our full offerings for SAP customers.

The increasing adoption of technologies like connected devices, augmented reality, and machine learning has changed the way we shop, and retailers are evolving how they do business to meet the needs of their customers.

Retailers say it’s no longer enough to keep pace with shoppers’ growing expectations—they must get ahead of them. That’s why more and more are turning to the cloud. They’re using it to eliminate data silos and take advantage of cloud-based analytics. They’re tapping into machine learning to improve all aspects of the value chain. And they’re making use of reliable and secure cloud infrastructure to scale their businesses.

Although every retail customer is different, many of them share similar objectives. Here are three major ways retailers take advantage of the cloud.

Storing and Analyzing Data in the Cloud

Data presents both a challenge and an opportunity for retailers. Which is why Ulta Beauty, the largest beauty retailer in the US, is moving to Google Cloud Platform (GCP). Now, with the help of BigQuery, Ulta Beauty will be able to more efficiently predict and analyze outcomes and develop more meaningful data insights that can be leveraged to deliver a more personalized, relevant guest journey.

They are not alone. DSW has also chosen to use GCP to help relaunch their DSW VIP loyalty program for the first time in over 10 years. With more than 90% of transactions running through their loyalty program, DSW needed a flexible and scalable solution to deliver a real-time loyalty program for 26 million active members. They’ve already seen a 9% uptick in new customers and have improved their already strong retention rate.

Improving Customer Experiences with AI and Machine Learning

Once retailers are able to access these insights, they are turning to AI to help personalize the overall shopping experience. At first, retail companies leveraged AI tools such as machine learning for product recommendations.

Now, more retailers use AI to forecast trends, predict inventory needs and prevent stock outs, and provide personalized recommendations to customers to intelligently and efficiently serve them.

Just look at METRO AG, one of the largest B2B wholesalers globally. They’re using AI and machine learning to better serve their customers. For example, many of their customers are restaurant owners. With Google Cloud AI capabilities, they can create tools that identify when a restaurant is out of a particular ingredient and automatically order more.

Ocado is another great example. The world’s largest online-only grocery retailer drove a 3.5% increase in contact center efficiency by using Google Cloud machine learning technology to respond to customer emails four times faster.

To help businesses further accelerate their AI solutions, Google has developed the Advanced Solutions Lab (ASL), which gives businesses the opportunity to work side-by-side with Google’s AI and ML experts to solve high impact challenges.

Fast Retailing, the Japanese retailer behind Uniqlo, is working with Google Cloud and ASL to help them better analyze customer data to forecast demand and deeply understand what their customers want.

Carrefour, one of the world’s leading retailers, also announced last year that its engineers will be working side-by-side with our AI experts to co-create new consumer experiences. This is in addition to deploying G Suite to their employees to support the company’s digital transformation.

Scaling Infrastructure to Meet Demand

Of course, none of this innovation is possible without a reliable infrastructure that can scale instantly to meet surges in traffic.

And many have found the reliability and security they need with the cloud. That’s why global cosmetics brand Lush chose Google Cloud. They migrated their e-commerce platform to GCP to handle increased traffic without compromising stability.

This move that ultimately reduced infrastructure hosting costs by 40 percent.

L.L.Bean also modernized its IT infrastructure by moving capabilities from its on-premises systems to GCP, improving customer satisfaction and IT efficiency across multiple sales channels.