A “one size fits all” approach to education no longer works in today’s classrooms. Using cloud-based technologies, schools and educators can take a more personalized approach to education–one that suits each student’s unique learning style, abilities, and needs. 

Taking the lead toward more equitable educational opportunities worldwide, education technology pioneer, LearningMate, is partnering with Google Cloud to offer intelligent, personalized learning via Google Cloud’s Student Success Services.

In a student survey by EDUCAUSE, a nonprofit association whose mission is to advance higher education through the use of information technology, nearly all respondents asked for more digital learning and study options. Given a list of educational material types, such as study guides and recorded lessons, 93 percent said they would like to have online access to at least two options and more than half (56 percent) chose seven or more. 

The trend towards student choice challenges educators to reconsider how they teach and support learners. Students expect the same qualities in their lessons as they encounter in their other, non-school related digital experiences: personalization; user-friendliness; and engagement. Educators who are used to a more top-down education model can struggle to meet these new expectations. 

Google Cloud created Student Success Services to help education institutions meet learners where they are—in the digital age. This suite of tools and services uses Google’s advanced artificial intelligence (AI) and analytic tools to:

• Engage with students via an AI-powered learning platform and interactive tutor
• Help educators and learners collaborate more effectively
• Provide current, actionable data on student progress 

To bring our Student Success Services to more schools and organizations worldwide, we’re partnering with LearningMate — a global leader in digital learning infrastructure. LearningMate is a key go-to-market partner, helping schools design, launch, and maintain their own learning infrastructure. To start, LearningMate is adding Google’s AI-powered learning platform to its Frost platform, a popular content management for education. 

As the education model continues to change and digital learning becomes the norm, disadvantaged students risk getting left behind. For these learners, the “digital divide” is very real, and stands to hinder them further–not only in school, but in society and the workplace in later life. 

“The gap will only widen between those with digital advantages and those who struggle to gain access to devices and network necessities,” EDUCAUSE states. To meet all these challenges, schools need a diverse mix of tools, methods, and partnerships.

To help close the digital divide among students, digital tools need to be able to scale up or down so institutions and districts of any size and budget can use them. Google Cloud designed Student Success Services with this ability in mind. By offering these services, LearningMate aims to ensure that even schools with smaller teams and fewer resources can offer individualized learning experiences to their students. 

With LearningMate and Google’s nearly 40 years of combined experience in education, this partnership aims to help educators better understand their students’ engagement, performance, and preferences. To learn more about Student Success Services and our collaboration with LearningMate, watch this session from our Government and Education Summit.

They say black never goes out of style. It’s something the team at Nuuly, URBN’s digital rental and resale business, know well. And it’s not just true of the company’s garments but their gadgets, too.

“I was having an offhand conversation with a UX designer recently,” Rebecca Sandercock, Nuuly’s strategy and insights manager, recalled in a recent interview from the company’s sunny, South Philly headquarters. “The designer was talking about how we had chosen dark mode for a number of interfaces internally because it actually saves so much on electrical output. They had the data to back that decision up, but more importantly, it’s just the kind of thing everyone is thinking about all the time here.”

Of course most every business is thinking about sustainability in some way these days. What makes Nuuly stand out is how quickly it can act, thanks in large part to the technology platform that’s made the entire enterprise possible.

“We’re kind of sustainable by our very nature,” Kim Gallagher, Nuuly’s director of marketing and customer success, said.

While she meant the rental and resale business, which helps customers buy fewer clothes and sees Nuuly items worn many times by many people—Gallagher could just as well have been referring to the sustainability inherent in, and enabled by, cloud computing.

There are the obvious, and oft-cited, advantages, such as how centralized data centers can operate more efficiently (some have been carbon neutral since the beginning). Yet there are even more subtle yet substantial benefits. In a marketplace and climate that are both changing faster and faster, sustainability requires a certain amount of agility. Such adaptability and scalability are intrinsic to the cloud technology that threads its way throughout Nuuly.

It turns out that being cloud native also means being sustainability native—as well as growth native. Since its 2019 launch, Nuuly’s net sales have risen roughly 6x over the first three fiscal years.

Cloud fits any situation

When URBN was developing Nuuly—launching in just 10 months—it chose to create everything from scratch on Google Cloud. Despite being part of a larger organization with decades of history and expertise, the company recognized the limitations presented by legacy systems and, more importantly, the necessity of building a wholly new platform that could be fully responsive.

The company has to react not only to new fashion trends but, crucially, the changing behaviors of customers. And not just their evolving tastes but also shopping habits, delivery preferences, unexpected customer service requests—is this a pattern, or a stain?—and social media chatter.

The pressure for a successful launch was high. The URBN portfolio, which also includes Urban Outfitters, Anthropologie, and Free People, had to keep evolving to satisfy a new generation of shopper who exists in an increasingly crowded and demanding digital marketplace.

The cloud’s responsiveness has thus proven its worth in creating a financially sustainable business as well as an environmentally sustainable one. Those even go hand in hand, as Gallagher points out: “Every customer who keeps renting is one who isn’t buying more occasion-specific clothes that go unworn most of the time.”

Dr. Alan Rosenwinkel, director of data science at URBN, had heard from his team about one garment that has become an emblem for the power of the platform. “It had been rented 25 different times before someone loved it enough to buy it and keep it forever,” he explained. (It’s also an emblem of the power of the cloud, that they would have the data awareness to track a single item so closely.)

It’s a new way of shopping made possible by a new way of computing. As one writer for Business Insider cheered, Nuuly “completely cured my addiction to fast-fashion.”

It makes for a healthy business, too. Sales for fiscal year 2019 exceeded $8 million and surpassed $24 million in 2020—one of the few URBN segments to grow during a tough year for fashion—and reached $47 million in 2021. The subscriber base had grown to 51,000 at the end of January.

Agility drives sustainability drives agility

For digital retailers to achieve such customer enthusiasm often relies as much on how the clothes get there as how they look. And those deliveries turn out to be a prime example of where Nuuly’s sustainability and technology meet.

For now, all shipping is handled through a state-of-the-art distribution center in Bucks County on the Philadelphia outskirts. Garments are shipped six-at-a-time, in fully reusable packaging, using ground transportation to keep the carbon footprint to a minimum. In certain limited geographic areas, shipments were sometimes taking longer than the two to five days most members would find acceptable.

“If we were an older company or weren’t set up from a technology perspective to be agile, we might have just said, ‘All right, we’re going to just go to three-day shipping for everyone,’” Rosenwinkel said. “That would drive up the cost, and the environmental impact. But we were able to be more strategic and more targeted about it.”

By regularly analyzing customer sentiment and retention data through BigQuery and Cloud Composer, and tying those to historical shipping times, Nuuly has been able to understand how shipping speed impacts its customers. Using a custom-built order management system, deliveries can be automatically adjusted to arrive more quickly, particularly when certain regions or days of the week are proving difficult to reach customers in time. These accelerated deliveries, like all Nuuly shipments, are made via UPS’s Carbon Offset program.

“Because we have the data, and the platforms to analyze it all,” Rosenwinkel said, “we’re delivering faster with the bare minimum impact on cost and emissions.”

It’s just one example of how modern retailers must juggle so many demands from consumers, workers, suppliers, and even regulators. Adding sustainability to that mix could be seen as a burden, but Nuuly shows how the right technology can lead to a holistic approach that makes all those interests work together even better.

And it allows for more opportunities and more kinds of sustainability, reaching from the designer’s atelier to the customer’s doorstep.

Sustainable details at every level

Back at the distribution center, workers can experience sustainability in a different way, as data is leveraged to enhance their well-being.

All workers are equipped with customized Android devices that help guide order tracking and fulfillment, plus cleaning, repairs, and reselling of garments as needs change throughout their lifecycle. Yet the insights go even deeper. The data science team has closely analyzed routes and repetitive motions for workers to keep their strenuous jobs as low-impact as the company’s broader environmental footprint.

“Through machine learning, we estimate we’ll be able to save our workers over 300,000 miles of steps over a five year period,” Rosenwinkel said. That’s enough walking to circumnavigate the globe 12.5 times.

The company has also applied ingenuity to one of the most notorious aspects of digital retail: packaging. Made from 100% post-consumer recycled materials like plastic bottles, Nuuly’s reusable carriers require no disposable bags or hangers to send goods back and forth. And once the packages have reached their end of life, the team is working with designers on ways to repurpose them into items that can then be offered for rental or sale on Nuuly.

“We’re really looking at the circular economy from every angle,” Gallagher said. “It’s built into the business.”

That includes not just the research the team did on-site at the Dry Cleaning & Laundry Institute—”We went to laundry school,” Gallagher jokes—but the digital tools that take those lessons even further. The team is always looking for ways to optimize fabric care, both to cut down on chemicals and water usage and extend the life of a garment. By analyzing the lifespan of every item, Nuuly not only makes them last longer but can identify problems faster. Employees even use custom apps to mark stains and damage so repair teams can more easily identify and fix the issues.

With its meticulous inventory tracking, Nuuly can even take marginal items, like a white gown or jeans with a small stain, and turn them into a custom dye job or an upcycling opportunity with a partner. These reworked items are then inserted back into the Nuuly Rent inventory as part of a growing collection of one-of-a-kind pieces, called Re_Nuuly.

Other services have launched quickly and easily thanks to the company’s cloud-enabled backend. Wanting to encourage more community and more reuse, the company created Nuuly Thrift. Debuting last fall after just a year in development, the team built everything from new interfaces to an evolved point of sale system.

It’s enabled Nuuly to offer many garments for rent or sale simultaneously, with “truly real-time inventories,” Rosenwinkel said. “So when it’s gone on one site, it shows up as gone on every site—no more surprises.”

Except for the good kind.

“I like to think we’re helping our customers think about ownership in a completely new way,” Sandercock said. “Once they run out of a use for a garment, they can offer it back, and sell it, and someone else will get to enjoy it and give it a new life. And Nuuly, we get to keep it in the community and keep it in the ecosystem, which is really cool—we’re taking extended responsibility over what happens to the clothing we create.”

When you choose to run your business on Google Cloud you benefit from the same planet-scale infrastructure that powers Google’s products such as Maps, YouTube, and Workspace. 

We have picked 10 ways in which Google Cloud Infrastructure services outshine alternatives in the market in how they simplify your operations, save money, and secure your data. 

1. Custom Machine Types means no wasted resources

Compute Engine offers predefined machine types that you can use when you create a VM instance. A predefined machine type has a preset number of vCPUs and a preset amount of memory; each type is billed at a set price as described on the Compute Engine pricing page. 

If predefined machine types don’t meet your needs, you can create a VM instance with a custom number of vCPUs and custom amount of memory, effectively building a custom machine type. Custom machine types are available only for general-purpose machine families. When you create a custom machine type, you are deploying a custom machine type from the E2, N2, N2D, or N1 machine family on GCP.  No other leading cloud vendor offers custom machine types so extensively.

Custom machine types are a good idea for workloads that aren’t a good fit for the predefined machine types and for workloads that require more processing power or memory but don’t need all of the upgrades provided by the next machine type level. This translates into lower operating costs.   They are also useful for controlling software licensing costs that are based on the number of underlying compute cores. 

Jeremy Lloyd, Infrastructure and Application Modernization Lead at Appsbroker, a Google partner: 

“Custom machine types coupled with Google’s StratoZone data center discovery tool provides Appsbroker with the flexibility we need to provide cost efficient virtual machines matched to a virtual machine’s actual utilization. As a result, we are able to keep our customers’ operating costs low while still providing the ability to scale as needed.”

2. Compute Engine Virtual Machines are optimized for scale-out workloads 

For scale-out workloads, T2D, the first instance type in the Tau VM family, is based on 3rd Gen AMD EPYC processors and leapfrogs VMs for scale-out workloads of any leading public cloud provider today, both in terms of performance and price-performance. Tau VMs offer 56% higher absolute performance and 42% higher price-performance compared to general-purpose VMs from any leading public cloud vendor (source). The x86 compatibility provided by these AMD EPYC processor-based VMs gives you market-leading performance improvements and cost savings, without having to port your applications to a new processor architecture. Sign up here  if you are interested in trying out T2D instances in Preview. 

For SAP HANA, Google Cloud has demonstrated with SAP how we can run the world’s largest scale-out HANA system in the public cloud (96TB).   With such innovation, you are covered as your business grows exponentially.

3. Largest single node GPU-enabled VM

Google is the only public cloud provider to offer up to 16 NVIDIA A100 GPUs in a single VM, making it possible to train very large AI models. Users can start with one NVIDIA A100 GPU and scale to 16 GPUs without configuring multiple VMs for single-node ML training, without crossing the VM layer. 

Additionally, customers can choose smaller GPU configurations—1, 2, 4 and 8 GPUs per VM—providing the flexibility to scale their workload as needed. 

The A2 VM family was designed to meet today’s most demanding applications—workloads like CUDA-enabled machine learning (ML) training and inference, for example. This family is built on the A100 GPU which offers up to 20x the compute performance compared to the previous generation GPU and comes with 40 GB of high-performance HBM2 GPU memory. To speed up multi-GPU workloads, the A2 VMs use NVIDIA’s HGX A100 systems to offer high-speed NVLink GPU-to-GPU bandwidth that delivers up to 600 GB/s. A2 VMs come with up to 96 Intel Cascade Lake vCPUs, optional Local SSD for workloads requiring faster data feeds into the GPUs and up to 100 Gbps of networking. A2 VMs provide full vNUMA transparency into the architecture of underlying GPU server platforms, enabling advanced performance tuning. Google Cloud offers these GPUs globally. 

4. ​​Non-disruptive maintenance means you worry less about planned downtime

Compute Engine offers live migration (non-disruptive maintenance) to keep your virtual machine instances running even when a host system event, such as a software or hardware update, occurs. Google’s Compute Engine live migrates your running instances to another host in the same zone without requiring your VMs to be rebooted. Live migration enables Google to perform maintenance that is integral to keeping infrastructure protected and reliable without interrupting any of your VMs. When a VM is scheduled to be live-migrated, Google provides a notification to the guest that a migration is imminent. 

Live migration keeps your instances running during:

• Regular infrastructure maintenance and upgrades
• Network and power grid maintenance in the data centers
• Failed hardware such as memory, CPU, network interface cards, disks, power, and so on. This is done on a best-effort basis; if a hardware component fails completely or otherwise prevents live migration, the VM crashes and restarts automatically and a hostError is logged.
• Host OS and BIOS upgrades
• Security-related updates
• System configuration changes, including changing the size of the host root partition, for storage of the host image and packages

Live migration does not change any attributes or properties of the VM itself. The live migration process transfers a running VM from one host machine to another host machine within the same zone. All VM properties and attributes remain unchanged, including internal and external IP addresses, instance metadata, block storage data and volumes, OS and application state, network settings, network connections, and so on. This has the benefit of reducing operational and maintenance overhead, helps you build a more robust security posture where infrastructure can be consciously revamped from a known good state and minimizes risks for advanced persistent threats. 

Refer to Lessons learned from a year of using live migration in production on Google Cloud from the Google engineering team.

5. Trusted Computing: Shielded VMs guard you against advanced, persistent attacks

Establishing trust in your environment is multifaceted, involving hardware and firmware, as well as host and guest operating systems. Unfortunately, threats like boot malware or firmware rootkits can stay undetected for a long time, and an infected virtual machine can continue to boot in a compromised state even after you’ve installed legitimate software. 

Shielded VMs can help you protect your system from attack vectors like:

• Malicious guest OS firmware, including malicious UEFI extensions
• Boot and kernel vulnerabilities in the guest OS
• Malicious insiders within your organization

To guard against these kinds of advanced persistent attacks, Shielded VMs use:

• Unified Extensible Firmware Interface (UEFI) BIOS: Helps ensure that firmware is signed and verified
• Secure and Measured Boot: Helps ensure that a VM boots an expected, healthy kernel
• Virtual Trusted Platform Module (vTPM): Establishes root-of-trust, underpins Measured Boot, and prevents exfiltration of vTPM-sealed secrets
• Integrity Monitoring: Provides tamper-evident logging, integrated with Stackdriver, to help you quickly identify and remediate changes to a known integrity state

The Google approach allows customers to deploy Shielded VMs with only a simple click, thereby easing implementation. 

6. Confidential Computing encrypts data while in use

Google Cloud was a founding member of the Confidential Computing Consortium. Along with encryption of data in transit and at rest using customer-managed encryption keys (CMEK) and customer-supplied encryption keys (CSEK), Confidential VM adds a “third pillar” to the end-to-end encryption story by encrypting data while in use. Confidential Computing uses processor-based technology that allows data to be encrypted in use while it is being processed in the public cloud. Confidential VM allows you to to encrypt memory in use on a Google Compute Engine VM by checking a single checkbox. 

All Confidential VMs support the previously mentioned Shielded VM features under the covers—you can think of Shielded VM as helping to address VM integrity, while Confidential VM addresses the memory encryption aspect which relies on CPU features. With the confidential execution environments provided by Confidential VM and AMD Secure Encrypted Virtualization (SEV), Google Cloud keeps customers’ sensitive code and other data encrypted in memory during processing. Google does not have access to the encryption keys. In addition, Confidential VM can help alleviate concerns about risk related to either dependency on Google infrastructure or Google insiders’ access to customer data in the clear. 

See what Google Cloud partners say about Confidential Computing here. 

7. Advanced networking delivers full-stack networking and security services with fast, consistent, and scalable performance

Google Cloud’s network delivers low latency, reduces operational costs and ensures business continuity, enabling organizations to seamlessly scale up or down in any region to meet business needs. Our planet-scale network uses advanced software-defined networking and security with edge caching services to deliver fast, consistent, and scalable performance. With 28 regions, 85 zones, and 146 PoPs connected by 16 subsea fiber cables around the world, Google Cloud’s network offers a full stack of layer 1 to layer 7 services for enterprises to run their workloads anywhere. Enterprises can be assured that they have best-in-class networking and security services connecting their VMs, containers, and bare metal resources in hybrid and multi-cloud environments with simplicity, visibility, and control. 

Google Cloud’s network has protected customers from one of the world’s largest DDoS attacks at 2.54 Tbps. With our multi-layer security architecture and products such as Cloud Armor, our customers ran their business with no disruptions. Furthermore, our recent integration of Cloud Armor with reCAPTCHA Enterprise adds best-in-class bot and fraud management to prevent volumetric attacks. Cloud Armor is deployed with our Cloud Load Balancer and Cloud CDN, extending the secure benefits at the network edge for traffic coming into Google Cloud so customers have security, performance, and reliability all built in. Furthermore, we are excited to offer Cloud IDS in preview, which was co-developed with security industry leader, Palo Alto Networks, to run natively in Google Cloud. 

Our advanced networking capabilities also extends to GKE and Anthos networking. With the GKE Gateway controller, customers can manage internal and external HTTPS load balancing for a GKE cluster or a fleet of GKE clusters with multi-tenancy while maintaining centralized admin policy and control. Unlike other Kubernetes offerings, we offer eBPF dataplane which brings powerful tooling such as Kubernetes network policy and logging to GKE. eBPF is known to kernel engineers as a “superpower” for its unique architecture to load and unload modules in kernel space, and now this capability is built in with Google Cloud networking. 

For observability and monitoring, our customers deploy Network Intelligence Center, Google Cloud’s comprehensive network monitoring, verification and optimization platform. With four key modules in Network Intelligence Center, and several more to come, we are working towards realizing our vision of proactive network operations that can predict and heal network failures, driven by AI/ML recommendations and remediation. Network Intelligence Center provides unmatched visibility into your network in the cloud along with proactive network verification. Centralized monitoring cuts down troubleshooting time and effort, increases network security and improves the overall user experience.  

8. Regional Persistent Disk for High Availability

Regional Persistent Disk is a storage option that provides synchronous replication of data between two zones in a region. Regional Persistent Disks can be a great building block if you need to ensure high availability of your critical applications as they offer cost-effective durable storage and replication of data between two zones in the same region. 

Regional Persistent Disks are also easy to set up within the Google Cloud Console. If you are designing robust systems or high availability services on Compute Engine, Regional Persistent Disks combined with other best practices such as backing up your data using snapshots enable you to build an infrastructure that is highly available and recoverable in a disaster. Regional Persistent Disks are also designed to work with regional managed instance groups. In the unlikely event of a zonal outage, Regional Persistent Disks allow continued I/O through failover of your workloads to another zone. Regional Persistent Disks can help meet zero RPO and near-zero RTO requirements and other stringent SLAs that your critical applications might require by maximizing application availability and protection of data during events such as host/VM failures and zonal outages. 

9. Cloud Storage’s single namespace for dual-region and multi-region means managing regional replication is incredibly simple

Similar to how Persistent Disk makes data more available by replicating data across zones, Cloud Storage provides similar benefits for object storage. Cloud Storage within a region is cross-zone by definition, reducing the risk that a zonal outage would take down your application. Cloud Storage adds to this by also providing a cross-region option that can protect against a regional outage and gets your data closer to distributed users. This comes in the form of Dual-region or Multi-region settings for a bucket. These are the simplest to implement cross-region replication offerings in the industry—just a simple button or API call to enable them. In addition to being simple to implement, they offer an added advantage of using a single bucket name that spans regions. 

This is unique in the industry. Competitive offerings currently require setting up and managing two distinct buckets, one in each region and they don’t offer the strong consistency properties Cloud Storage offers across regions. Operations and app development are burdened by this design. Google’s single namespace approach dramatically simplifies application development (the app runs on single region or dual/multi-region without any changes), and provides simpler application restarts and testing for DR.

10. Predictive autoscaling 

Customers use predictive autoscaling to improve response times for applications with long initialization times or for applications with workloads that vary predictably with daily or weekly cycles. When you enable predictive autoscaling, Compute Engine forecasts future load based on your Managed Instance Group’s history and scales out the MIG’s in advance of predicted load, so that new instances are ready to serve when the load arrives. Without predictive autoscaling, an autoscaler can only scale a group reactively, based on observed changes in load in real time. 

With predictive autoscaling enabled, the autoscaler works with real-time data as well as with historical data to cover both the current and forecasted load. Forecasts are refreshed every few minutes (faster than competing clouds) and consider daily and weekly seasonality, leading to more accurate forecasts of load patterns.

For more information, see How predictive autoscaling works and Checking if predictive autoscaling is suitable for your workload.

These are just a few examples of customer-centric innovation that set Google Cloud infrastructure apart.  Bring your applications and let the platform work for you.   

Get started by learning about your options for migration, or talk to our sales team to join the thousands of customers who have embarked upon this journey.

Acknowledgement

Special thanks to Dheeraj Konidena (Google) for contributing to this article.

The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.

A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. 

Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.

Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. 

Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. 

Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. 

Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.

Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.

Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”