In times of significant disruption, organizations are faced with three choices: Retrench within legacy solutions, pause and do nothing while waiting for more data or different circumstances, or press ahead, potentially even accelerating to realize the desired outcome. In such an environment, it is critical to ensure you’re delivering the greatest possible impact to the business. 

In Google Cloud’s Office of the CTO, or OCTO, we have the privilege of co-innovating with customers to explore what’s possible and how we can re-imagine and solve their most strategic challenges. These collaborative innovation engagements are often core to critical transformational projects, which often include the rehosting, evolution, and at times re-architecture of existing business solutions. 

We are happy to offer this brand new white paper, where we have distilled the conversations we’ve had with CIOs, CTOs, and their technical staff into several frameworks that can help cut through the hype and the technical complexity, to help devise the strategy that empowers both the business and IT. We called one such framework “up or out.” (And we don’t mean some consulting firm’s hard-nosed career philosophy.) 

One model that we found can help enterprises chart their cloud adoption journey delineates cloud migration along two axes—up and out, and we’ll cover this in much greater detail in the white paper itself.

As you can see, there isn’t a single path to the cloud—not for individual enterprises and not even for individual applications. The up or out framework can help an IT organization and its leadership characterize how they can best benefit from migrating their services or workloads. The framework acts as a general pattern that highlights the continuum of approaches to explore, and you can learn all about it by downloading this detailed white paper.

Or, if you’re really ready to jump start your migration today, you can take advantage of our current offer by signing up for a free discovery and assessment.

In the United Kingdom, the popularity of online grocery shopping is expected to surge from about 6% of the market today to 9% by 2021, according to market research firm Mintel. One of the pioneers of online-only grocery retailing is Ocado, based in Hatfield, Hertfordshire in the U.K. Since starting commercial deliveries in 2002, the company has grown to 600,000 active customers, 260,000 weekly orders, and £1.39 billion in annual revenue.

Ocado takes supermarket trips out of the equation by enabling shoppers to purchase items online through its convenient web and mobile applications. Items are then picked and packed in automated warehouses and shipped directly to customers in a one-hour time slot of their choosing. Ocado’s delivery punctuality is 95%, order accuracy is 99%, and its service footprint now reaches more than 70% of the U.K. population.

“Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

—Paul Clarke, Chief Technology Officer, Ocado

The company achieved its success by building in-house almost all the technology and automation that powers its end-to-end e-commerce, fulfillment, and logistics platform. Ocado also developed a new platform, the Ocado Smart Platform (OSP), which offers large brick-and-mortar grocery retailers around the world access to a best-in-class solution for online grocery.

Democratizing machine learning

The shopping journey for online grocery retailing differs significantly from other e-businesses. Customers often buy dozens of products at once, a single household may have multiple buyers using multiple devices, and product shelf life may only be a couple of days.

“We often say that having built an end-to-end platform that can do online grocery scalably and profitably, we can do other forms of online retail; but the reverse does not necessarily follow,” says Paul Clarke, Chief Technology Officer at Ocado. “Google Cloud Platform gives us the flexibility and performance to tackle the large and complex data challenges unique to our business.”

The Ocado business model takes advantage of consumers’ shifting preferences and the links between digital technology and shopping experiences.

“Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

—Paul Clarke, Chief Technology Officer, Ocado

The company has been building machine learning into its systems for over five years. Until recently, Ocado machine learning applications required specialist data scientists, typically with PhDs in machine learning, who would build these solutions from the ground up. It also required the specialist who set up the system and costly on-premises infrastructure to train and run these systems.

However, working with Google as a private alpha testing site for Google Cloud Machine Learning Engine accelerated its adoption of artificial intelligence (AI).

“We’ve been talking about how the cloud could democratize AI for some time,” says Paul. “Google Cloud Machine Learning Engine gives us the agility we need. Our developers were able to try out TensorFlow and see firsthand the benefits of machine learning in the cloud.”

TensorFlow is an open source software library for machine learning developed by the Google Brain team. Ocado developers, engineers, and data scientists now use TensorFlow for many of their machine learning projects. They deploy the models they build on Google Cloud Machine Learning Engine, which lets them train models faster across servers, desktop computers, and mobile devices through a single application program interface (API). Additionally, Google Cloud Machine Learning Engine integrates easily with the other Google Cloud Platform products used widely at Ocado.

What do customers really want?

One of the first TensorFlow models Ocado created was a machine learning algorithm that tags and categorizes customer emails and then prioritizes them for response.

The contact center receives thousands of emails each day and Ocado wanted to automate determining which ones needed to be answered immediately and which ones could wait.

For example, a first-time customer expressing their delight in using Ocado doesn’t need to be responded to with the same urgency as a customer who is missing an item from their order or who won’t be home to receive the delivery.

“Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

—James Donkin, General Manager, Ocado

“We get a lot of emails from customers saying, ‘Our service was great,’ or ‘The driver was very courteous,'” says James Donkin, General Manager, Ocado. “But when issues like weather or road conditions potentially affect delivery, we often get surges of urgent questions. Enabling agents to respond without having to sort through less-urgent emails improves Ocado’s responsiveness and customer service.”

Using Google Cloud Machine Learning Engine, TensorFlow, and a large data set culled from several years’ worth of manually categorized customer emails, Ocado experimented on which kind of neural network architecture would best prioritize emails. After testing its models, Ocado implemented the highest-performing one and has been able to respond to urgent messages four times faster. The company also discovered that 7% of its emails don’t require a response at all, which means call center representatives now have more time to devote to higher priority messages.

“Without Google Cloud Machine Learning Engine, it would have been a lot harder to succeed on a project like email classification,” says Roland Plaszowski, who has recently managed several big data projects and initiatives at Ocado.

“Even if we invested significantly in infrastructure, it would be difficult to manage because of the computational intensity. It’s challenging and expensive to run machine learning projects at the same time without infrastructure that you can scale easily.”

Ocado also uses machine learning to predict customer behavior and improve experiences. By analyzing order data, Ocado makes shopping as frictionless as possible. For example, the ordering system can pre-populate customers’ shopping carts with items they are most likely to purchase, remind customers about items they may have forgotten, and notify them of multi-buy offers they haven’t completed, for example, only buying one of a buy one, get one free offer. Based on machine learning from previous purchase data, the Ocado system can also offer new products that are likely to delight customers.

“You will regularly see items that are more personally relevant to you instead of items that are being promoted more generally,” says James. “I’m a vegetarian, so I’m offered specials for vegetarian products that I normally buy and new ones that I’ve never bought. I’m also less likely to see things that I’m not interested in.”

Machines and machine learning

Within the Internet of Things (IoT), Ocado is looking to enhance its warehouse robots with machine learning. An integral part of the OSP, thousands of robots continually stream data into Google Cloud Storage and Google BigQuery.

Ocado data scientists apply machine learning to create a type of swarm intelligence that enables warehouse robots to work cooperatively to achieve a common goal. Projects include modules to search robot telemetry data, such as whether a battery pack is operating within standard tolerances or whether firmware has been successfully loaded, and use it to optimize maintenance schedules or detect patterns in wear and tear.

“Another challenge we’re looking at is how to embed machine learning directly into robots so they become smarter in terms of self-testing, exception handling, and error recovery,” says Paul. “This is a challenging combination of IoT, data analytics, and machine learning that we believe Google BigQuery and Google Cloud Machine Learning are particularly well suited to helping Ocado achieve.”

Scaling for new business

Scalability is also a major reason behind some of Ocado’s cloud initiatives, including the migration of all its on-premises data to the cloud. Ocado wanted to improve customer experiences, empower business teams with greater insight, and reduce IT overhead, so it consolidated onto Google Cloud Platform.

“The old databases just weren’t fast enough,” says Paul. “We needed a solution that could scale with the amount of data we generate and how we use it. Google Cloud Storage and Google BigQuery now provide the backbone, from a data point of view, for the Ocado Smart Platform.”

Ocado estimates its business, product, and transaction data is approaching two petabytes. Combining customer and supply chain data helps both internal Ocado operations and the company’s ambitions to commercialize OSP.

“When compared with other options for expansion internationally, selling OSP as a managed service lets us turn companies that could have been competitors into customers,” says Paul. “We want to build OSP once and then turn it on for multiple business-to-business customers.”

Each time Ocado adds a new hosting customer to OSP, it will launch a customized instance to fit that customer’s requirements. The capacity and performance of each new OSP instance must be able to scale quickly as the backend platform for established retailers with large numbers of products, customers, and transactions.

Ocado’s first OSP customer, Morrisons, is already benefiting from this first-of-a kind solution. Morrisons is one of the UK’s four largest supermarkets and uses OSP to power its online retail business. Using Google Cloud Platform, Ocado has stored, processed, and analyzed terabytes of Morrisons’ data using a dedicated data lake and Google BigQuery.

In addition to using Google Cloud Platform for OSP, Ocado also adopted it for its own online grocery retail business operation. Ocado originally used the Apache Spark and Apache Hadoop open-source frameworks on Google Compute Engine for its data platform. Moving to Google BigQuery frees Ocado business analysts from the complex query setup and workflows associated with Spark and Hadoop. Plus, it lets Ocado share data analytics with suppliers and partners.

Google BigQuery is well integrated with TensorFlow on Google Cloud Machine Learning Engine and Google Cloud Dataproc, the Apache Spark and Apache Hadoop service that lets Ocado use open source data tools for batch processing, querying, streaming, and machine learning. Google Cloud Dataflow and Google Cloud Dataproc handle cluster management, and provide an easy-to-use framework so developers can spend less time and money on administration and more time on delivering valuable business features.

Switching from Hadoop to Google BigQuery revealed a series of cost and performance improvements. For example, Ocado no longer needed to decide how many instances to bring up in a cluster or wait for the instances to spin up. Google handled everything.

“We simply ran our queries and paid for the resources that we use,” adds Roland. “One big win with Google BigQuery is we don’t have to do maintenance. Best of all, we saw Google BigQuery outperform our Hadoop cluster by over 80 times on our largest dataset, and for only two-thirds the cost.”

The Opportunity for Streamed AR/VR Content 

What if you could get a high quality AR/VR experience without a dedicated physical computer—or even without a physical tether?  

In the past, interacting with VR required a dedicated, high-end workstation and, depending on the headset, wall-mounted sensors and a dedicated physical space. Complex tasks in VR can push the limits of sensor reach, cable-length, and spatial boundaries, entangling the artist and restricting their movement. This solution was not scalable beyond a handful of advanced use cases.

Recently, tetherless VR headsets from manufacturers such as HTC and Oculus have emerged that free the user from these odious restrictions, enabling a new freedom to experience VR and AR from just about anywhere. The enhanced portability and reduced cost has led to substantially increased adoption among consumers and opened up many new exploratory use cases in enterprise.

However, while these headsets are more accessible and portable, a tradeoff in compute power was required in order to achieve these goals. The limited on-device compute power of this new generation of Head Mounted Displays (HMDs) is acceptable for many consumer applications such as casual gaming. However, advanced enterprise workloads with heavy graphics, compute, or memory requirements can be difficult or even impossible to run on these lightweight devices.

By combining NVIDIA CloudXR with their powerful GPUs running NVIDIA RTX Virtual Workstations (vWS) in Google Cloud data centers, you can experience high-fidelity VR and AR applications from just about anywhere with a good internet connection. The heavy computations are performed in the cloud on a GPU-attached VM and the content streams to any CloudXR compatible headset. 

The combination of Google Cloud’s private fiber optic network — the same network we built for global delivery of YouTube content — and CloudXR’s QoS technologies provides the user with the highest possible quality of service. In fact, the streaming experience is comparable to that of a headset tethered to a powerful physical workstation, but without the friction of hardware and cables. 

This combination of power and portability sets the stage to unlock the potential of high-quality gaming and enterprise AR/VR experiences anywhere on Earth.

Case Study: Creating a Masterpiece

Digital character creation is a core skill for many 3D artists today. One of the preferred methods of 3D character creation is digital sculpting, which lets artists create both hard-surface and organic shapes with high levels of accuracy and control.

Sculpting is one of the many tasks in the character designer’s skillset. Artists must also master the texturing, rigging, and posing of characters as part of the digital character creation pipeline. 

Mastering all these tasks can be challenging, and often, the technology gets in the way; working in multiple applications requires switching contexts and workflows, breaking the artist’s creative flow. The traditional user experience can also be unintuitive, forcing the creator to translate what they want to do with their hands and head into mouse movement and keyboard presses.

Masterpiece Studio Pro revolutionizes this character creation workflow by giving artists the first fully immersive 3D creation pipeline. Artists work in VR, giving them a far more intuitive and seamless way to work which combines the best of the digital and physical worlds.

In Masterpiece Studio Pro, the artist can work on the character or object at any scale, using familiar tools and hand gestures to sculpt a model, much as they would a real clay figure. Performing other tasks such as skeleton creation is simple, allowing the artist to work directly with the limbs of a character to place and adjust the joints.

Bringing It All Together

NVIDIA CloudXR streaming from the cloud provides tremendous opportunities for new creative use cases within gaming and across the enterprise. This solution joins Virtual Studio for Gaming as the latest in our series to help developers build better games.

The Masterpiece Studio use case is a powerful demonstration of new modalities for content creation and collaboration. To try CloudXR with NVIDIA RTX vWS on Google Cloud for yourself, see this tutorial. Masterpiece Studio Pro also offers extensive learning materials and a free trial.

Today, we’re announcing that we are collaborating with Sparkle and others to build and operate two submarine cable systems linking the Middle East with southern Europe and Asia: the Blue Submarine Cable System connecting Italy, France, Greece, and Israel; and the Raman Submarine Cable System connecting Jordan, Saudi Arabia, Djibouti, Oman and India. 

Developing additional network capacity and routes is critical to Google users and customers around the globe, who depend on robust connectivity to power their online lives, and communicate with friends, family and business partners. Google users and Google Cloud customers will benefit from increased capacity and decreased latency to regions in the area. 

Each equipped with 16 fiber optic pairs, the Blue and Raman Submarine Cable Systems are expected to be ready for service in 2024. In time, consortium members hope to make additional landings and connect the two systems through terrestrial network assets.

Like with other infrastructure projects, building a subsea cable is an opportunity to pay tribute to a regional luminary who has advanced human understanding. The Raman cable is named for Sir Chandrasekhara Venkata Raman, an Indian physicist who won the 1930 Nobel Prize in Physics—the first Asian to receive that honor in science. C.V. Raman’s work centered on light scattering, which finds that when light traverses a transparent material, some of the deflected light changes wavelength and amplitude. This so-called Raman effect is a foundational principle in the field of optics that enables any underwater network cable. A trip across the Mediterranean also prompted him to ask why the sea is blue, when water itself is clear? Thanks to C.V. Raman, we now know that the sea isn’t simply reflecting the sky, but because the water itself causes blue light to scatter.      

With Blue and Raman, we now have 18 investments in subsea cables around the world, including Google-funded cables like Curie, Dunant, Equiano, Firmina and Grace Hopper, and consortium cables like Echo, JGA, INDIGO and Havfrue. You can learn more about Google Cloud’s network and infrastructure here.