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.

Editor’s note: February is Black History Month—a time for us to come together to celebrate the diverse set of experiences, perspectives and identities that make up the Black experience. Over the next few weeks, we will highlight Black-led startups and how they use Google Cloud to grow their businesses. Today’s feature highlights Acclinate and its founders, Del and Tiffany. 

As patients, as caregivers, and as parents taking our own children to the doctor, we want recommended medications to be safe and effective. It’s a right everyone deserves.

It’s known that certain medications don’t work in the same way in all populations. For example, Albuterol, a medication often prescribed for asthma, is less effective in 67% of all Puerto Ricans and 47% of Black Americans. These problems—which can have deadly consequences—result from historically limited diversity in pharmaceutical clinical trials. 

We founded our startup Acclinate to integrate culture and technology to achieve more inclusive clinical research. Help pharmaceutical companies and healthcare organizations access and engage communities of color so research is more inclusive.

Bridging the health equity gap by building trust

It’s important that health research organizations access and engage communities of color so their efforts reflect all the people they serve. Take a disease like diabetes, which affects a significantly higher proportion of Black Americans. When you look at even recent clinical trials for diabetic drugs, the representation of Black Americans among participants is only in the low single digits, despite comprising 13 percent of the U.S. population and more than 40 percent of diabetes patients in this country. Industry leaders have been aware of the lack of diversity issue, but some have chosen to ignore it or brush it aside. The biggest problem, in our opinion, is that there have been no penalties for not achieving higher diversity figures in clinical trials, and only minor financial repercussions to pharma/biotech companies when their treatments either do not work across all groups once approved, or there is a lack of uptake by all groups due to the lack of testing in those groups. The lack of clinical trial diversity has adversely impacted the reputation of the industry and the ability to recruit diverse populations in the future. 

Acclinate integrates culture and technology to promote diverse patient representation in medical research. Our approach is not transactional. We build trust through our  #NOWINCLUDED community, which is an ongoing, ever-expanding digital platform that educates and engages with communities of color on health issues.

#NOWINCLUDED includes a website app, and social media presence where members can learn information about diseases, particularly those with greater negative impacts on people of color, such as cancer, diabetes, and cardiovascular diseases. Members can share stories and ask questions. By providing access to trusted resources about these health issues and the latest clinical research, we empower Black people to take control of their health and consider  participating in research that is shaping the future of healthcare.  

For healthcare-related organizations, we offer the opportunity to better understand the attitudes, aspirations, and unmet needs of underrepresented minority communities. Data from #NOWINCLUDED feeds our HIPAA-compliant SaaS platform, e-DICT™ (Enhanced Diversity in Clinical Trials), which uses predictive analytics and machine learning to identify individuals matching the requirements and most likely to be receptive to participation in a particular clinical trial.

Acclinate scales its platform with Google Cloud

We rely on Google Cloud services, including Vertex AI, to know whom to ask, when to ask, and how to ask for clinical trial participation. With Vertex AI, we enjoy a unified platform for developing our artificial intelligence models, including tools for preparing and storing our datasets. We can easily train and compare models using AutoML, which requires minimal ML expertise or effort with its intuitive graphical interface. This allows us to leverage more than ten ordinal and categorical data points to determine in real time a community member’s likelihood to enroll, which we call our Participation Probability Index (PPI). Our models evolve in an iterative process the more we interact with, and learn about, our community members.

We follow the pay-per-use Google Cloud Platform architecture model using serverless technology, which helps reduce infrastructure management costs and lets us focus on product development and engaging with communities across the U.S.

CloudSQL, a fully managed relational database service, integrates easily with BigQuery so we can glean insights for our clients in real time, all with Google Cloud’s robust security, governance, and reliability controls. Virtual Private Cloud (VPC) gives us scalable and flexible networking for our cloud-based resources and services. We also use Identity and Access Management (IAM) to simplify oversight of Google Cloud resource permissions for different user groups and roles, with appropriate security protections. 

API Gateway manages our APIs using Cloud Functions, which both use consumption-based pricing, plus give our developers consistent and highly secure access to our services through a well-defined REST API. We use Memorystore for Redis to reduce platform latency. This is done with a fully managed service powered by the Redis in-memory data store, which builds application caches for fast data access. All of this comes together to provide an outstanding experience for our platform’s users and contributors.

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Expanding influence with the Google for Startups Accelerator for Black Founders

Three months of one-on-one Google support and mentorship as part of the most recent Google for Startups Accelerator : Black Founders cohort not only helped us build our product, but also helped us earn external credibility. People use Google every day, so whether we’re trying to engage in conversations with industry experts or with somebody in a rural community, it is helpful to have the buy-in of a globally-recognized brand as we take on a historically difficult, systemic issue with challenges around trust. Getting access to the products, best practices, and people we need to build and grow through the Accelerator program has been priceless. For example, working with the Google AdWords team helped us generate important traffic from people interested in learning more about #NOWINCLUDED or sharing their story with us. Jason Scott, who leads the Google for Startups Accelerator: Black Founders program, is still connecting us to people in his network and identifying key opportunities for us months after the program wrapped. He continues to demonstrate that he is invested in seeing us succeed. 

Our company has made great progress against our goals, in part thanks to receiving capital from the Google for Startups Black Founders Fund. We received $100K in non-dilutive funding along with Google Cloud credits, Google.org Ads grants, and hands-on support. We used the funds to pay for the transition and development costs associated with moving to Google Cloud. The Google support and accountability has been incredible. After receiving the Google for Startups Black Founders Fund award, we’ve gone on to raise another $1M and moved our cloud from Salesforce to Google Cloud. 

We also had the amazing opportunity to be selected as one of six companies to take part in a face-to-face web conference with Sundar Pichai, Google’s CEO. We were thrilled to hear him explain his vision around health equity and the role Google plays. Ultimately, for us, it’s not just about the funding we get, but we are also gratified to receive support from an entity that truly believes in addressing this issue. We know Google is aligned with our mission of health and racial equity. 

Championing diversity in clinical trials

Our 2022 looks bright. We expanded our presence to Washington, D.C. as part of the Johnson & Johnson Innovation JLABS ecosystem. We were also selected to take part in the BLUE KNIGHT initiative created between Johnson & Johnson Innovation and the Biomedical Advanced Research and Development Authority (BARDA) under the U.S. Department of Health and Human Services. 

Acclinate is also on track to have contracts with five of the top 25 largest biopharmaceutical companies in the U.S this year. They’ve taken note, as has the Food and Drug Administration (FDA), that the lack of diversity in clinical trials represents a significant health concern—to the extent that the FDA has provided strong guidance for pharmaceutical companies to  diversify their clinical trials. At the same time, the industry is also responding to pressure from communities of people of color to make equitable representation a priority.

Today, we are in the fortunate but challenging position to have significant inbound opportunities coming our way. In response, we continue to recruit and hire talented people to join our team. On the technology side, we are happy to be aligned with Google Cloud to have powerful cloud infrastructure that will scale with us, as well as high-caliber champions united in partnership. With people’s lives at stake, we are passionate in our commitment to helping ensure medications do what they are supposed to do: heal and improve the quality of life for everyone who takes them. 

Hear Acclinate cofounders Del Smith and Tiffany Whitlow chat with Google’s Head of Startup Developer Ecosystem Jason Scott and fellow Black Founders Fund recipient Bobby Bryant about building on Google Cloud in a recent Google for Startups Instagram Live. 

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more

Since 2014, tech companies have relied on metrics to identify trouble spots, establish baselines, and measure meaningful progress in diversity, including publishing DEI data directly through diversity annual reports. However, as our understanding of DEI has evolved over time each company’s report has diverged, creating a fragmented landscape of industry-wide data. This separation is problematic as no single company’s diversity dataset can solve tech’s DEI challenges. Instead, we need to build industry-wide systemic solutions to create sustainable change, and those start with establishing a common language for DEI data and standardizing and sharing DEI data across tech companies.

The challenge is most companies publish diversity data in a way that makes it difficult to analyze, such as using bar graphs and pie charts. Researchers, as a result, are unable to easily pull the real, detailed data or aggregate and analyze it for their unique needs (e.g. hiring trends for tech vs. nontechnical roles, representation of Asian women in leadership, and more). As we shared in our most recent Diversity Annual Report, external research shows that sustainable change will only come from solutions that encompass the entire tech industry and data transparency is a critical step in this work.

Google’s Diversity Annual Report public dataset & BigQuery

In May, we released our 2022 Diversity Annual Report, which includes demographic data on workforce representation, hiring, and attrition of employees at Google, including leadership. You can see our hiring data by race/ethnicity, gender, and intersectional hiring over time, by region, and more. In an effort to make this data more transparent and accessible for analysis, we have added it as a public dataset in BigQuery, Google Cloud’s powerful data warehousing tool. Our data is now even easier for researchers, community organizations, and industry groups to leverage and compare against external benchmarks to help contextualize our progress.

As one of the first in the industry, we are proud to have published our diversity data on BigQuery for the second year in a row. Our dataset, among others, is public, stored and paid for by Google so those who are interested can use BigQuery’s advanced analytic capabilities through the Google Cloud Public Dataset Program for free*.

Contextualizing diversity data is necessary to make meaningful conclusions

While Google’s diversity dataset can help users compare their own datasets to Google’s current and historical trends, DEI data is only useful when analyzed in the context of other relevant datasets. For example, concluding that Black+ hiring has increased from 8.8% to 9.4% has little meaning unless there is a point of reference, like US general census data, labor force participation rates or graduation rates. It’s why Google also includes other public datasets in BigQuery such as related industry DEI data, talent and graduation pools. By doing so, users can run a sample query that then compares Google’s hiring and representation to related industries (software publishers, data process services, etc.). Users can then better understand and contextualize areas of progress and opportunity, and they can more objectively identify where organizations can take a proactive role in addressing not only DEI in their companies, but also in the communities in which they call home.

Intersectional data and disaggregated baseline metrics

In the workplace, intersectional data is key to understanding the layers of exclusion and inequity that may exist for certain groups. This includes those with social identity overlap, like race, gender, and LGBTQ+, that create multiple levels of inequality or discrimination. It’s critical for DEI data to be disaggregated in a meaningful way to diagnose the true health of a system and to better understand how intersectionality contributes to the greater DEI landscape .

Google’s Diversity Annual Report public dataset includes Google’s intersectional hiring and representation data also broken down by tech, non-tech, and leadership roles. BigQuery’s friendly interface makes it easy to select the relevant parameters and join this data with other public or private datasets to help meaningfully contextualize Google’s data against the broader industry. Anyone from data scientists to DEI stewards can launch public datasets from the Google Cloud Marketplace and start querying them right away. Findings can be visualized through tools like Looker, Data Studio, or Tableau.

This is just the beginning

As DEI work continues to evolve into an industry-wide approach, we must encourage a standard of practice for collecting and reporting data across the board. At scale, data has the power to enable the tech industry to make real improvements collectively, in addition to inside our individual companies. We hope our dataset and sample queries give researchers and individuals a launchpad to become DEI practitioners of the tech industry, and ensure we have the right data to solve the right problems.

Footnote:

*The only time anyone would need to pay is for queries performed on the data after BigQuery’s 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset.

When Blue Apron had issues running its data warehouse on another cloud provider, it built an analytics platform using Looker and Google BigQuery to enable faster business decisions about food inventory.

Blue Apron’s mission is to make incredible home cooking accessible to everyone. Launched in 2012, Blue Apron is reimagining the way that food is produced, distributed, and consumed.

Industries: Retail & Consumer Goods

Location: United States

Google Cloud Results

• Enables near real-time business decisions to better manage food inventory and delivery by reducing query times exponentially
• Helps improve customer service and optimize business processes with faster insights
• Reduces costs while reclaiming up to a week per month of engineering

The popularity of meal kit delivery services has surged in recent years as consumer attitudes toward home cooking and grocery shopping have shifted. As a pioneer in the category, Blue Apron helps its customers create incredible home cooking experiences by sending culinary-driven recipes with high-quality ingredients and step-by-step instructions straight to customers’ doors. Blue Apron also offers a monthly wine subscription service and a la carte culinary tools and products through its marketplace.

If that sounds simple, it isn’t. Ingredients for the meal kits must be sourced at the right time, quality, and price. Orders must be packed efficiently and in exactly the right proportions. Most importantly, meal kits must be delivered to the customer fresh and on time.

To meet these criteria and make data meaningful and intuitive to its managers, one of the tools Blue Apron relies on is Looker, an analytics platform that lets business users explore data and ask sophisticated questions using familiar terms.

A Google Cloud partner and winner of the 2016 Google Cloud Global Partner Award for Solution Innovation, Looker integrates its solution with Google Cloud Platform to help customers modernize their analytics.

Blue Apron previously used Looker with a single database instance hosted on another cloud provider. As data volumes grew and queries became more complex, it became difficult to scale. Blue Apron’s only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. To improve speed, scalability, and cost efficiency, Blue Apron moved its data warehouse to Google BigQuery.

“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster,” says Sam Chase, Tech Lead, Data Operations at Blue Apron. “Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”

The analytics platform of the future

When you’re making business decisions about a customer’s dinner, speed matters. Looker takes full advantage of the power of Google BigQuery, making it easy to build a data exploration platform. Blue Apron’s applications publish event data to Kafka—approximately 140 million events per day—and data is then streamed into Google BigQuery, which performs lightning-fast queries on both streamed and static data. Now, business users and analytics teams can make decisions based on near real-time information in Looker, instead of waiting until the next business day for results.

“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day,” says Sam.

Previously, Blue Apron spent up to a week out of every month optimizing its data warehouse to attempt to improve query performance. With Google BigQuery, all maintenance is handled by Google, reclaiming 25% of up to two engineers’ time. Even when multiple people are using Looker concurrently, query performance never degrades and storage never runs out.

“Because Google BigQuery is architected as a giant, shared cluster, growth is smooth,” says Lloyd Tabb, Founder and CTO of Looker. “Like a race car going from 0 to 120 mph, there are no shift points, just smooth acceleration. To us, it looks like the future.”

An empowering, integrated toolset

Looker takes advantage of aggressive caching and support for date-based table partitioning in Google BigQuery to increase performance, simplify the load process, and improve data manageability. By partitioning data by time, Blue Apron can also take advantage of better long-term storage pricing without sacrificing query performance. When using Google BigQuery with Looker, analysts can easily see how much data is going to be scanned before each query is run.

Blue Apron is also using Looker for Google BigQuery Data Transfer Service to provide actionable analytics for all of the company’s Google marketing data from Google AdWords and DoubleClick by Google in one place to understand campaign performance across channels, saving its data operations team months of work. Using Looker Blocks, marketers can quickly make sense of the data with reports and dashboards, and set alerts when campaign performance hits certain thresholds.

Looker Blocks for Google AdWords and DoubleClick by Google provide all the analysis you’d get straight from the Google console, plus additional value-add analysis that’s impossible to replicate without SQL. Complex metrics such as ROI on ad spend, flexible multi-touch attribution, and predictive lifetime value empower marketers with a better understanding of their customers and where to spend their next dollar.

In addition to these turnkey dashboards and pieces of analysis, marketers can customize views to meet their unique needs and workflows. These capabilities help the Blue Apron marketing team make decisions regarding the allocation of spend to maximize customer acquisition and retention.

“Everyone at Blue Apron is excited about using Google BigQuery with Looker,” says Sam. “Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”

For data cleansing and transformation, Blue Apron uses Google Cloud Dataproc to run fully managed Apache Spark clusters on Google Cloud Platform. It’s also leveraging Google BigQuery integration with Google Workspace to bring data into Google Sheets for further distribution and analysis.

“Transferring data between Google tools is fast because it all happens on the Google network,” says Sam. “We can pull data from Google BigQuery, run transformations with Spark, and then write it back to Google BigQuery. That’s very helpful in providing our business users and data analysts with the richest, most current data.”

A perfect match for better insights

As Blue Apron seeks to expand its reach and deepen its engagement with customers, it is making Google BigQuery and Looker available to more users, providing a high-quality interactive analytics experience. “Our ability to pull a lot of data in and compute fast results affects everyone in our company,” says Sam. “Using Google BigQuery and Looker to iterate quickly and build new models to make our operations more efficient will directly impact our customers.”

For Looker, Google BigQuery represents the next step in data warehouse evolution. “Google BigQuery is a perfect match for Looker, combining easy setup with near infinite scale-out and elasticity,” says Lloyd. “People can make smarter decisions faster that directly benefit their business and customers.”

While there is uncertainty about how much the climate will change in the future, we know it won’t look like the past. Extreme weather events will increase in frequency and severity; the world will continue to warm, and the cost of climate change will increase.

Government plays a vital role in understanding and responding to these changes quickly. Achieving this improved response time will require data insights to ensure informed decision-making—from local to global scales. The challenge is not only urgent; it’s one of the world’s biggest “big data” problems.

Fortunately, new technologies to help us monitor the Earth are proliferating. Thousands of satellites take millions of images of the planet every day. Sensors generate data about temperature, precipitation, wind, soil conditions, and more—as frequently as every second. We have more information about the planet’s systems than at any other time in history. And the data will only continue to grow. The problem is not the lack of data–it is harnessing this data to drive insights for decision makers to tackle climate change. That’s why Google Cloud has partnered with Climate Engine.

How Climate Engine and Google Cloud enable greater climate resilience

Climate Engine is a scientist-led company that works with Google to accelerate and scale the use of Google Earth Engine’s world-class geospatial capacities (in addition to those of Google Cloud Storage and BigQuery, among other tools) in support of climate action in the public sector. Powered by Google Cloud’s infrastructure, Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities, enabling scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface. 

With cloud-based technologies, we can leverage massive computing at a scale that generates actionable insights from Earth-based data. These insights help us better manage resources, understand risks, predict changes, and respond to disasters as we meet the challenge of climate change. Geospatial AI combines the power of artificial intelligence (AI) and machine learning (ML) with geospatial analysis. Google Cloud’s Geospatial AI solutions provide departments and agencies with a centralized system to collect, process, and deliver Earth-based data into decision-making contexts.

Climate Engine and Google Cloud provide specialists with the opportunity to go back in time and see how our landscapes have changed due to changes in climate and other human activities over the past few decades. Years of data can now be quantitatively analyzed and visualized in a matter of a few seconds, enabling government agencies to fulfill their mandates by drawing invaluable insights into how landscapes are changing, what physical and natural assets are at risk, and where the opportunities are for reducing emissions and increasing carbon sequestration. 

“This is game changing for natural resource managers and scientists at public institutions at all levels of government,” says Dr. Daniel McEvoy, regional climatologist, at the Desert Research Institute & Western Regional Climate Center, Nevada System of Higher Education.https://www.youtube.com/embed/aPGsi8bd_Zk?enablejsapi=1&

Use cases for geospatial climate information systems

The use cases for this technology are as varied as the climate challenges themselves. These include monitoring, predicting, and analyzing the risks of extreme weather events like floods, wildfire, drought, extreme heat, wind, and other climate hazards. Use cases also include tracking changes in ecosystems, disease vectors, water availability and quality, soil health, growing seasons, air pollution, and more. These use cases are some of the ways that Google Cloud and Climate Engine can help the public sector deliver on government mandates. These provide insights that are helpful for a wide range of departments, and that can be applied in spatial and temporal scales that are meaningful for governments to take action.

“Our planet is changing at a rate that we have never experienced,” says Forrest Melton of the NASA Western Water Applications Office. To respond to these changes, we must understand what is happening across a wide range of environmental variables and at geospatial scales that range from local to global. We now have access to more data about the planet than ever before. The big challenge is converting data into actionable insights and then rapidly integrating these insights into decision-making systems. Climate Engine and Google Cloud help resolve this problem through innovative analytical tools and effective use of cloud computing.” 

Climate change carries an existential risk to our current and future stability and security. Together, we are working to provide transformational technologies that help meet that risk and build a safer, more resilient future for all of us. 

Learn more about Google Cloud’s environmental initiatives here and here.