In the past decade, the Healthcare and Life Sciences industry has enjoyed a boon in technological and scientific advancement. New insights and possibilities are revealed almost daily. At Google Cloud, driving innovation in cloud computing is in our DNA. Our team is dedicated to sharing ways Google Cloud can be used to accelerate scientific discovery. For example, the recent announcement of AlphaFold2 showcases a scientific breakthrough, powered by Google Cloud, that will promote a quantum leap in the field of proteomics. In this blog, we’ll review another omics use case, single-cell analysis, and how Google Cloud’s Dataproc and NVIDIA GPUs can help accelerate that analysis.

The Need for Performance in Scientific Analysis

The ability to understand the causal relationship between genotypes and phenotypes is one of the long-standing challenges in biology and medicine. Understanding and drawing insights from the complexity of biological systems abounds from the actual code of life (DNA) through to expression of genes (RNA) to translation of gene transcripts into proteins that function in different pathways, cells, and tissues within an organism. Even the smallest of changes in our DNA can have large impacts on protein expression, structure, and function, which ultimately drives development and response – at both cellular and organism levels. And, as the omics space becomes increasingly data- and compute-intensive, research requires an adequate informatics infrastructure. An infrastructure that scales with growing data demands, enables a diverse range of resource-intensive computational activities, and is affordable and efficient – reducing data bottlenecks and enabling researchers to maximize insight. 

But where do all these data and compute challenges come from and what makes scientific study so arduous? The layers of biological complexity begin to be made apparent immediately when looking at not just the genes themselves, but their expression. Although all the cells in our body share nearly identical genotypes, our many diverse cell types (e.g. hepatocytes versus melanocytes) express a unique subset of genes necessary for specific functions, making transcriptomics a more powerful method of analysis by allowing researchers to map gene expression to observable traits. Studies have shown that gene expression is heterogeneous, even in similar cell types. Yet, conventional sequencing methods require DNA or RNA extracted from a cell population. The development of single-cell sequencing was pivotal to the omics field. Single-cell RNA sequencing has been critical in allowing scientists to study transcriptomes across large numbers of individual cells. 

Despite its potential, and the increasing availability of single-cell sequencing technology, there are several obstacles: an ever increasing volume of high-dimensionality data, the need to integrate data across different types of measurements (e.g. genetic variants, transcript and protein expression, epigenetics) and across samples or conditions, as well as varying levels of resolution and the granularity needed to map specific cell types or states. These challenges present themselves in a number of ways including background noise, signal dropouts requiring imputation, and limited bioinformatics pipelines that lack statistical flexibility. These and other challenges result in analysis workflows that are very slow, prohibiting the iterative, visual, and interactive analysis required to detect differential gene activity. 

Accelerating Performance

Cloud computing can help not only with data challenges, but with some of the biggest obstacles: scalability, performance, and automation of analysis. To address several of the data and infrastructure challenges facing single-cell analysis, NVIDIA developed end-to-end accelerated single-cell RNA sequencing workflows that can be paired with Google Cloud Dataproc, a fully-managed service for running open source frameworks like Spark, Hadoop, and RAPIDS. The Jupyter notebooks that power these workflows include examples using samples like human lung cells and mouse brains cells and demonstrate acceleration between CPU-based processing compared to GPU-based workflows. 

Google Cloud Dataproc powers the NVIDIA GPU-based approach and demonstrates data processing capabilities and acceleration, which in turn have the potential of delivering considerable performance gains.  When paired with RAPIDS, practitioners can accelerate data science pipelines on NVIDIA GPUs, reducing operations like data loading, processing, and training from hours to seconds. RAPIDS abstracts the complexities of accelerated data science by building upon popular Python and Java libraries effortlessly. When applying RAPIDS and NVIDIA accelerated compute to single-cell genomics use cases, practitioners can churn through analysis of a million cells in only a few minutes.

Give it a Try

The journey to realizing the full potential of omics is long; but through collaboration with industry experts, customers, and partners like NVIDIA, Google Cloud is here to help shine a light on the road ahead. To learn more about the notebook provided for single-cell genomic analysis, please take a look at NVIDIA’s walkthrough. To give this pattern a try on Dataproc, please visit our technical reference guide.

Given the May 1 deadline for students to enroll, colleges and universities have been carefully watching the numbers of students who put down deposits and commit to a school. Like nearly every other sector in the U.S., colleges and universities have been hit hard by the pandemic and economic downturn, making enrollment numbers more important than ever to maintain financial health and meet student body goals. Technology and AI-based models are providing a way for institutions to manage their admissions data efficiently and cost-effectively.

For example, an admissions officer may want to offset drops in international or out-of-state enrollment by attracting local talent so they can achieve their target class profile. Additionally, institutions have sought to increase the availability and access of higher ed and are intentionally recruiting more low-income and underrepresented populations. New technologies allow admissions officers to have a stronger grasp of the incoming class’ details. With the press of a button, users can switch between overall, in-state, out-of-state, and international enrollment figures. The “How Do Regions Compare to Last Year” tile lets users view the percentage of change and the total number of candidates by region, comparing 2020 and 2021 side-by-side. With access to data, regional recruiters can focus on individual candidates and develop a plan to change the numbers.

An innovative enrollment strategy at MSU

Michigan State University was looking to improve how they managed the recruiting process. They implemented an enrollment strategy that saw their out-of-state enrollment increase by over 20% and generate $5M in additional net tuition revenue in a single year. “We knew we needed better real-time and predictive insights about our enrollment pipeline, and we understood the value of bringing in external data on prospects, but we couldn’t do that by ourselves. 

The solution really helped us be innovative with our analytics and improve our enrollment outcomes,” said someone close to the project at Michigan State University.

Data-driven insights support informed enrollment decisions

Student admissions and marketing groups are turning to Google Cloud and Deloitte to help support their enrollment decisions with predictive, actionable insights across recruiting and admissions processes. The partnership resulted in Candidate360, a solution that helps institutions process and analyze large amounts of data and develop meaningful insights to support their enrollment goals and mission. Part of Google’s Student Success Services offerings, Candidate360 uses artificial intelligence and predictive analytics to help higher education institutions improve enrollment and matriculation, as well as optimize financial aid decisions.

With nine AI/ML models, Candidate360’s capabilities help university leaders, enrollment teams and recruiters do things like:

• Identify regions with clusters of candidates or see dips in application numbers
• Deploy marketing resources and admissions staff to the right geographic areas 
• Understand applicants’ needs in terms of academics, safety, and social activities
• Make intelligent financial aid decisions to recruit and retain talented students from underrepresented communities
• Respond faster to the students who are most likely to be accepted and then go on to enroll, stay enrolled, and graduate
• Work towards the ideal class composition and increase competitiveness with peer institutions

Enhancing the student experience

Enrollment is the first key touchpoint with incoming students, and tools like Candidate360 can help institutions build a student preferences profile throughout the application process. They can understand each student’s prospective major, areas of interest, dream job, and more, allowing colleges and universities to personalize recommendations and advising for enrolled students.

With integrated toolsets, Candidate360 and Google Cloud’s Student Success Services can help colleges and universities emerge from the pandemic stronger than ever. 

To see a demonstration of Candidate360 in action, contact our sales team. 

Some of the most important data at your company isn’t living in databases, but in documents, and most business processes begin, involve or end with a document. 

Yet most companies are still manually entering data and reliant on guesswork to make sense of it all as the volume and variety of data explodes. Organizations are also leaving heaps of value on the table in the form of new and better customer experiences that can be unlocked with artificial intelligence (AI) applied to documents. 

The latest releases of Document (Doc) AI platform, Lending DocAI and Procurement DocAI, built on decades of AI innovation at Google, bring powerful and useful solutions to these challenges. Under the hood are Google’s industry-leading technologies:

• Computer vision (including OCR) and Natural Language Processing (NLP) that creates pre-trained models for high-value, high-volume documents. 
• Google Knowledge Graph to validate and enhance the fields in your documents.
• Training and creation of your own custom document models. 
• Human interaction with AI to ensure accuracy where needed.

Google Cloud DocAI platform, Lending DocAI and Procurement DocAI are now generally available. Thousands of customers have tried these products in the preview phase—and DocAI has already processed tens of billions of pages of documents across lending, insurance, government and other industries.

“The vast majority of enterprise content still resides in unstructured sources like documents. Google Cloud’s Document AI brings a fresh new perspective to the problem informed by the company’s decades of experience making sense of the largest unstructured corpus in the world—the world wide web.” —Ritu Jyoti, VP of AI Research, IDC

Cut document processing costs by up to 60%

Lending DocAI helps banks, mortgage brokers and other lending institutions fast track the loan application process from weeks to days, dramatically reducing the cost of issuing a loan. And Procurement DocAI enables companies to automate procurement data capture at scale, lowering processing costs by up to 60%.

These solutions are built on DocAI platform, a unified console for document processing that lets you quickly access all parsers and tools. From the platform, you can automate and validate documents to streamline workflows, reduce guesswork, and keep data accurate and compliant. 

Get more value from AI with DocAI’s industry-specific solutions

According to Accenture’s AI: Built to Scale report: “Companies that scale successfully see 3x the return on their AI investments compared to those who have not fully rolled out AI capabilities.” 

Core to our strategy at Google Cloud is the creation of industry-specific solutions that help companies get maximum value out of their investments in AI. We announced Lending DocAI, our first solution designed specifically for the financial services industry, at the Mortgage Bankers Association convention last year. It processes borrowers’ income and asset documents using a set of specialized machine learning (ML) models, and automates routine document reviews so that mortgage providers can focus on more important work. 

Lending DocAI is now generally available and includes more specialized parsers for critical loan documents including paystubs, bank statements, and more. Our goal is to provide the right tools to help borrowers and lenders have a better experience and close home loans faster. For more, watch this video.

Procurement DocAI is also now generally available. This solution helps companies accelerate document processing for invoices, receipts, and other valuable documents in the procurement cycle. 

Automating data capture is helping our customers increase accuracy and also lower their procure-to-pay processing costs. We are continually expanding the types of documents Procurement DocAI can process—the latest is a utility parser for electric, water and other bills. In addition, Procurement DocAI leverages Google Knowledge Graph to validate and enrich parsed information to make the data even more useful. Check out this overview video for more details. 

One company that lives and breathes AI-enabled document management is AODocs. It uses Procurement DocAI to simplify invoice processing for enterprise customers and launched a new Gmail add-on, Invoice to Sheet, for SMB customers who just want to track their invoices in Google Sheets.

“Google Cloud’s Procurement DocAI service allows our document management platform to better automate the processing of invoices; AODocs customers who have tested our new account payables workflow estimate that the productivity of their A/P team has more than doubled, thanks to the reduction of manual data input brought by the Procurement DocAI.”—Stéphan Donzé, Founder and CEO, AODocs

The new specialized parsers for Lending and Procurement DocAI can be used alongside our existing AutoML Text & Document Classification and AutoML Document Extraction services. These technologies provide a state-of-the-art toolset for creating new document models and have been widely deployed by customers in financial services and other industries. 

Partner to accelerate your AI deployment and results

Having the right partner to ease the complexity of rolling out your AI-strategy in mortgage document processing is critical to transforming your customers’ experience. We’re excited to announce a partnership with Mr. Cooper, a leader in mortgage servicing, to provide customers with more automation and workflow tools throughout their entire mortgage life cycle. As part of this agreement, both companies will collaborate on digitizing Mr. Cooper’s core mortgage platform, creating a more personal customer experience utilizing AI, and driving a broader culture of innovation to imagine and develop services and solutions that will transform the mortgage experience for American homeowners.

“Over the last few years, we have made substantial investments in our servicing technology and core mortgage platform that have revolutionized the customer experience, while providing dramatic efficiencies in operating cost. Our partnership with Google Cloud AI will build on those advances and help make these technologies available for the mortgage industry.” —Jay Bray, Chairman and CEO, Mr. Cooper Group

This builds upon the robust partner ecosystem we’re creating to help customers revolutionize the home loan experience, which includes last year’s partnership announcement with Roostify.

Integrate human review into ML predictions 

Next up is the general availability of Human-in-the-Loop AI, a new DocAI feature that will help companies achieve higher document processing accuracy with the assurance of human review. Adding human review can increase accuracy and help businesses interpret predictions using purpose-built tools to enable those reviews. 

Processing documents quickly and cost-effectively is important. But it’s often necessary to have a high level of assurance on data accuracy for compliance. CIOs and IT decision-makers need highly accurate ML predictions to fulfill compliance requirements, improve employee experience (e.g. less rework), and raise customer satisfaction (e.g. fewer data errors). Including human participation in ML processes allows AI and humans to work together for the best possible results.

Human-in-the-Loop AI provides the workflow to manage human review tasks and produces a percentage confidence score of how “sure” it is that the AI ingested the document correctly. Document AI extracts data from documents with ML, and when paired with Human-in-the-Loop AI, human reviewers are able to verify the data captured. This system is customizable, providing the flexibility to set different thresholds and assign individual groups of reviewers to various stages of the workflow. With Human-in-the-Loop AI, developers can choose trusted reviewers to assign to the task; these reviewers can be from within their own or partner organizations.

More Document AI resources

To learn more, check out the Document AI webpage and watch a demo of how to process sample forms in AI Platform notebooks to inspect data extraction and confidence scores. For more on how customers and partners like Workday, AODocs, and Mr. Cooper are using Document AI, listen to our fireside chat. And stay tuned for the exciting evolution of these technologies in future releases of DocAI.

Editor’s note: In Today’s guest post we hear from beauty leader L’Oréal about their approach to building a modern data platform on fully managed services: managing the ingest of diverse datasets into BigQuery with Cloud Run, and orchestrating transformations into relevant business domain representations for stakeholders across the organization. Learn more about how businesses have benefited from Cloud Run in Forrester’s report on Total Economic Impact.

L’Oréal was born out of science. For over 100 years, we have always shaped the future of beauty, and taken its eternal quest to new horizons. This has earned us our current position as the world’s uncontested beauty leader (~€ 32 B annual sales in 2021), present in 150 countries with over 85,000 employees.

Today, with the power of our game-changing science, multiplied by cutting-edge technologies, we continue our lifelong journey of shaping the future of beauty.

As a Beauty Tech company, we leverage our decades-long heritage of rich data assets to empower our decision-making with instant, sophisticated analysis.

Because we oversee global brands, which must adapt to local requirements, we need to maintain a deep understanding of what a brands’ data represents, while managing disparate legal and regulatory requirements for different countries. Our end goal is to run a safe, compliant and sustainable data warehouse as efficiently and effectively as possible.

We sync and aggregate internal and external data from a wide variety of sources across organizations and retail stores. This made the management of our data warehouse infrastructure used to be very complex and hard to manage before Google Cloud. L’Oréal’s footprint was so large that we once found it impossible to have a standardized method to handle data. Every process was vendor-specific, and the infrastructure was brittle. We went looking for a solution to our complex data infrastructure needs, and defined the following non-negotiable principles:

• No Ops: The job of a developer at L’Oréal is not to manage servers. We need an elastic infrastructure that scales on demand, so that our developers can focus on delivering customized and inclusive beauty experiences to all consumers, rather than focusing on managing servers.
• Secure: We have strict security and compliance requirements which vary by country, and we employ a zero-trust security strategy. We must keep both our own internal data and customer data safe and encrypted.
• Sustainable : Our data lives in multiple environments, including on-prem data centers and public cloud services. We must be able to securely access and analyze this data while minimizing the complexity and environmental impact of moving and duplicating data.
• End-to-end supervision: Because developers shouldn’t be managing servers, we need a “single pane of glass” dashboard to monitor and triage the system if something goes wrong.
• Easy-to-deploy: Deploying code safely should not compromise velocity. We are constantly developing innovations that push the boundaries of science and reinvent beauty rituals. We need integrated tools to make our code deployment process seamless and safe.
• Event-driven architecture: Our data is used globally by research, product, business and engineering teams with high expectations on data quality and timeliness. Many of our internal processes and analysis are based on near real-time data.
• Data products delivered “as a service”: We want to empower our employees to drive business value at record speed. To that end, we need solutions that enable us to remove the developers from the critical path of solution delivery as much as possible.
• Extract-load-transform (ELT): Our goal is to implement the pattern to load data as soon as possible into the data warehouse to take advantage of SQL transformations.

After considering multiple vendors on the market, with these principles in mind, we landed on end-to-end Google Cloud serverless and data tooling. We were already using Google Cloud for a few processes, including BigQuery, and loved the experience.

We’ve now expanded our use of Google Cloud to fully support the L’Oréal Beauty Tech Data Platform.

L’Oréal’s Beauty Tech Data Platform incorporates data from two types of sources: directly via API, which is data that adapts easily to our schema and is inserted directly into BigQuery, and bulk data from integrations, which require event-driven transformations using Eventarc mechanisms. These transformations are performed in Cloud Run and Cloud Functions (2nd gen), or directly in SQL. With Google Cloud, we can adapt very quickly.

Today, we currently have 8500 flows for ~5000 users using the native zero-trust capabilities offered by Google Cloud. Indeed, the flows come from Google Cloud and other third-party services.

BigQuery enabled us to adopt standard SQL as our universal language in our data warehouse and meet all expectations for queries and reporting. We were also able to load original data using features like federated queries, and efficiently transitioned from ETL to ELT data ingestion by handling semi-structured data with SQL. This approach of loading original data from sources into BigQuery with non-destructive transformations allows us to reprocess data for new use-cases easily, directly within BigQuery.

Our applications are hosted on multiple environments – on-premises, in Google Cloud, and in other public clouds. This made it difficult for our data engineers and analysts to natively analyze data across clouds until we started using BigQuery Omni. This capability of BigQuery allowed us to globally access and analyze data across clouds through a single pane of glass using the native BigQuery user interface itself. Without BigQuery Omni, it would’ve been impossible for our teams to natively do cross-cloud analytics. Moreover, it eliminated the need for us to move sensitive data, which is not only expensive because of local tax and subsea transport, but also incredibly risky – sometimes even forbidden – because of local regulations.

Today Google Cloud powers our Beauty Tech Data Platform, which stores 100TB of production data in BigQuery and processes 20TB of data each month. We have more than 8000 governed datasets, and 2 millions of BigQuery tables coming from multiple data sources such as Salesforce, SAP, Microsoft, and Google Ads.

For more complex transformations where custom and specific libraries are required, Cloud Workflows help us to manage the complexity very efficiently by orchestrating steps in containers through Cloud Run, Cloud Functions and even BigQuery jobs — the most used way to transform and add value to the L’Oréal data.

Additionally, by using BigQuery and Google Cloud’s serverless compute for API ingestion, bulk data loading, and post-loading transformations, we can keep the entire system in a single boundary of trust at a fraction of the cost. With ingest, queries, and transformations all being fully elastic and on-demand, we no longer have to perform capacity planning for either the compute or analytics components of the system. And of course these services’ pay-as-you-go model perfectly aligns with L’Oréal’s strategy of only paying for something when you use it.

Google Cloud fulfilled the requirements of our Beauty Tech Data Platform. And as if offering us a no-ops, secure, easy-to-deploy, custom-development free, event-based platform with end-to-end supervision wasn’t enough, Google Cloud also helped us with our sustainability efforts.

Being able to measure and understand the environmental footprint of our public cloud usage is also a key part of our sustainable tech roadmap. With Google Cloud Carbon Footprint, we can easily see the impact of our sustainable infrastructure approach and architecture principles. Our Beauty Tech platform is a strategic ambition for L’Oréal: inventing the beauty products of the future while becoming the company of the future.

Sustainable tech is an imperative and a very important step towards this ambition of creating responsible beauty for our consumers, and sustainable-by-design tech services for our employees. We all have a role to play, and by joining forces, we can have a positive impact.

Google Cloud’s data ecosystem and serverless tools are highly complementary, and made it possible to build a next-generation data analytics platform that met all our needs.

Get started using serverless and BigQuery together on Google Cloud today.