After a seamless migration to Google Cloud Platform with CloudCover and Google Cloud Professional Services, Hike has reduced its costs by 20% and processed analytics queries 20 times faster than with its previous cloud provider. The business is also using AI and machine learning to enhance the experience provided by a new sticker-based messaging app, Hike Sticker Chat.

India is a market of opportunity for businesses that provide messaging apps to consumers. With more than 1.3 billion people, the country is the second most populous in the world. However, global messaging app providers face a robust market challenge from Hike, a home-grown internet and technology startup. Launched in 2012, Hike provides innovative products such as Hike Messenger and more recently the AI- and machine-learning-enabled Hike Sticker Chat, a service that enables young people in the country to express themselves through digital stickers.

The business says it understands the people of India and communication like no one else, while its mission is to reduce individuals’ dependency on the keyboard. To do this, Hike is building one of the largest repositories of AI and machine-learning-enabled stickers for Hike Sticker Chat. This messaging platform is, according to Hike, the only product of its type that enables conversations through stickers covering more than 40 languages and local dialects.

Google Cloud Results

• Processes analytics queries 20X faster than previously
• Doubles compute throughput
• Uses Google Cloud Machine Learning Engine managed, distributed capabilities to train complex models on TensorFlow that provide delightful local sticker recommendations through Hike Sticker Chat

Founded by Kavin Bharti Mittal, the Delhi-based venture is backed by SoftBank, Tencent, Tiger Global, Foxconn, and Bharti. To date, Hike has raised $261 million in funding. In August 2016, Hike raised its Series D round of funding, led by Tencent and Foxconn, at a valuation of $1.4 billion. The business is one of the fastest Indian startups to achieve Unicorn status, doing so in less than four years.

Hike started operations on a multinational cloud service. However, as user numbers and usage grew, the business began exploring options to improve performance and stability, reduce costs, and cut administration loads. In particular, Hike wanted to reduce latency between cloud data centers.

Focus on product development

“We aimed to move away from a technology stack with single points of failure to a horizontally scaled, highly reliable, distributed systems and managed services environment that enabled us to focus on product development rather than operations,” says Aditya Gupta, Director, Engineering, Hike.

Hike then began exploring the opportunities presented by Google Cloud Platform. The business held a number of executive-level meetings with Google to understand the capabilities, roadmap, and track record of the cloud service. It then decided to proceed with a proof of concept with Google Cloud Premier Partner CloudCover.

The proof of concept revealed that when Cloud Load Balancing was operating, latency between the Google Cloud data center in Taiwan and Delhi, India, was less than the latency between the incumbent cloud provider’s data center and Delhi. Further, compute throughput was up to two times greater on Compute Engine than on the equivalent service, while Hike could complete more then 1 million connections on Compute Engine – up from 500,000 connections on the incumbent service.

Migrate to GCP

The success of the exercise prompted Hike to migrate its messaging app to Google Cloud Platform. “We chose Google Cloud Platform because of its very broad set of services and features,” explains Gupta. “In addition, Google’s innovation mindset and the richness of the partnership would allow us to be onboarded quickly to machine learning services such as Cloud Machine Learning Engine.”

The business called on Google Cloud Professional Services (Technical Account Management) to help ensure a seamless lift-and-shift migration over two months. Google Cloud Professional Services initially undertook a technical infrastructure kickoff to establish a foundation for architecture requirements such as identity and access management and security.

Google Cloud Professional Services team delivers smooth migration

Google Cloud Professional Services worked closely with Hike to map out and deliver the Google Cloud Platform architecture that would deliver the greatest value to the business. The Professional Services team also worked with Hike to resolve product and support queries quickly; provided project background for product and support teams; and organized project meetings and early adopter program access.

In addition, Professional Services team members worked on site at least once a week, coordinated external support during critical migration periods, and coordinated teams in five countries for a single, 17-hour migration marathon. Over 60 days, the business migrated 7,000 processor cores, running virtual machine instances used for messaging infrastructure and analytics, to Google Cloud Platform.

Throughout the exercise, Google Cloud Professional Services worked with CloudCover to educate the customers’ technology teams to achieve proficiency with Google Cloud Platform. The teams soon built up skills and knowledge of best practices and began applying them to the Google Cloud Platform environment.

The Hike Google Cloud Platform architecture comprises virtual machine instances running in Compute Engine; Cloud Storage for unified object storage; networking; a BigQuery analytics data warehouse; Cloud Dataflow to transform and enrich data; Cloud Load Balancing to distribute workloads to maximize efficiency; and Cloud Dataproc to run Hadoop clusters.

Hike is also stepping up its AI & machine learning capabilities. It uses Google Cloud Machine Learning Engine managed, distributed computing capabilities to train complex models on TensorFlow. This powers key use cases such as delightful local sticker recommendations on Hike Sticker Chat. Hike is also investing heavily on AI and machine learning research.

Hike has achieved a range of benefits from its Google Cloud Platform deployment. As well as reduced latency, improved compute throughput, and increased connection handling, Google Cloud Platform managed services have enabled the business to reduce the time and effort required to administer core infrastructure, with the saved resources allocated to improving its messaging product.

“Managed services are beginning to reduce our operational overheads,” says Gupta. “For example, managed instance groups and Cloud Load Balancing are reducing our instance count and costs, thereby reducing involvement from DevOps and developer teams.”

Google Cloud Platform 20% cheaper

Gupta and his team have calculated that running for three years on Google Cloud Platform will cost, including the cost of migration, 20 percent less than on its previous platform. BigQuery is processing queries 20 times faster than a similar service offered by the previous provider, while storing 125TB of data and streaming 1.5TB of data daily. Furthermore, Hike’s analytics pipeline costs 80 percent less than in its previous environment.

“Google Cloud Platform has played an important role in enabling us to continue to innovate and realize our mission of reducing dependency on the keyboard,” says Gupta.

Climate scientists face a deluge of environmental data to analyze and interpret from real-time sensors and satellites across the globe. The stakes are as high as our planet’s long-term future, but rapidly changing conditions are already impacting communities through extreme events like floods and wildfires as well as management of everyday resources. In this context, detailed environmental maps are key sources for urgent global issues like food security, water quality, and vegetation levels.

Scientists, researchers, and developers rely on state-of-the-art cloud computing tools like Google Earth Engine (EE) to detect changes, map trends, and quantify differences on the Earth’s surface. EE leverages the compute power of Google Cloud to combine a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.

To find out more about how climate scientists address these complex research challenges, I spoke to Dr. Richard Fernandes, Research Scientist at Natural Resources Canada (NRCan). Dr. Fernandes is also a 2022-23 member of the Google Research Innovators Program, which provides technical and professional support to a global cohort of leading researchers. I asked him to describe how cloud computing and machine learning (ML) tools can support climate resilience research and drive awareness about climate sustainability.

Dr. Fernandes, can you give me an overview of your research in climate sciences?

My research focuses on mapping the status and trends of vegetation over Canada. Every month we generate maps of vegetation parameters, such as canopy cover at 20m resolution, to support environmental monitoring and assessment. These maps contribute to global datasets that are used to reduce uncertainty in weather and climate forecasts.

Canada has approximately 10 million square kilometers of land and the annual data volume of these maps is equivalent to streaming HD movies for over 750 hours non-stop. And that’s only the tip of the iceberg: the volume of input data we need to generate those maps is typically 100 times more. Unlike movie streaming services, we have to independently process each input pixel to locate it accurately, screen for clouds (and even the shadows of clouds), and then transform it into a vegetation parameter value like canopy cover using ML algorithms.

The volume of high-resolution data and the amount of compute we need are challenging and ever-increasing. Rather than dedicating servers 24/7 for constant monitoring, we rely on cloud computing and ML. Cloud computing allows us to manage all this data in a useful and accessible way. We have also been able to successfully use the Google Artificial Intelligence (AI) Platform to calibrate new ML models for third-party datasets.

How did you start working with Google Cloud?

I started using EE together with open source APIs for integration with Google Drive about four years ago. With the pandemic my research group has transitioned to using EE, Google Cloud, and Google Drive for both our Canada-wide mapping as well as for R&D activities. In January we developed and released the LEAF toolbox, which builds on EE satellite data to create customizable maps of foliage density in near real-time. We do all of the pixel processing in EE and leverage the ability to integrate our own functions using their APIs. We combine EE with Google Cloud to handle and manage output datasets. Fortunately, EE has most of the input data already at hand so we don’t need to deal with that 100x larger volume.

What impact do you expect LEAF will have now–and down the road?

Both the Canadian federal government and provinces already use our data products as inputs to permafrost, crop status, and water resource models. Agriculture Canada had been using a conventional Geographic Information System (GIS) and approached us to ask how we manage our data. They want to use LEAF to assess how crops are progressing, which impacts local economies and the global food supply. We’re in a pilot with them to apply EE to their crop condition assessments.

Also exciting is that the Province of Alberta is integrating the LEAF toolbox within a system for monitoring the reclamation status of oil and gas wells and mines. They want to know how to rehabilitate sites that were developed for mineral and gas deposits. This is another great use case but many others are possible. The technology itself is cool, but that’s not even the point. ML algorithms are constantly evolving and improving. New ML algorithms use active learning that detects mistakes and makes updates on the fly. The technology will be different in five years — or five months! — so our priority is making these tools accessible and useful now.

Why is state-of-the-art technology for climate research so important?

Scientific research must be validated, transparent, and rigorous to drive the best solutions to our complex and changing ecosystem. Climate action needs greater public awareness, which requires more knowledge, which demands the best data. By democratizing information and decision-making, we can create an ecosystem of openness and public-private partnerships. We can lower the barrier to entry for advanced research and help scientists validate and reproduce their results. Also, scientists don’t want to have to become software engineers for these custom highly specific solutions. They want user-friendly tools that let them focus on their analyses and share their insights with the world. That’s one of the major appeals of the Google Research Innovators Program for me: I want to share LEAF with colleagues and collaborate with other researchers who are using new tools in new ways.

Do you have any parting words about your mission?

We’re so fortunate to have Canadian taxpayers and the Government of Canada funding our work. We have worked with many scientists over the past two decades to develop and validate the algorithms we use, especially Fred Baret and Marie Weiss at INRA France, who have championed the idea of free and open access to algorithms and knowledge, and data.

I really believe in democratic access to information. I like the fact that the terms of service of Google products allows us to offer not only maps but the actual processing system in a free and open manner to everyone. I also like that EE provides a simple-to-use user interface that works on mobile devices. We designed LEAF not just for experts, but for individuals. My mom was able to make maps of a nearby park in real time on her tablet. It is my hope that expanding access to critical environmental information will increase our collective awareness of how our actions impact both near and far places — and make us active in the cause of a sustainable future.

That’s an inspiring note to end on. Thank you for your time!

Thanks for having me!

Click here to learn more about Google’s commitment to renewable energy. Or try the LEAF toolbox for yourself!

May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.

Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.

CultivatePeople 

Founder: Lola Han

Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees. 

Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after  our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.” 

Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission. 

My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and: 

 1) Enjoy the journey by celebrating wins and giving yourself credit;

2) Follow your intuition—it’s (almost) always right; 

3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);

4) Give regular words of affirmation to employees on even small achievements.”

Swit

Founder: Josh Lee and Max Lim

Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.

Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”

Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope  to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.” 

Check out more from Swit in their founder story.

WISY 

Founder: Min Chen

Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.

Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.

Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“

Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.

My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.

Every year at CES, people from around the world experience the latest and greatest that consumer tech has to offer. In 2021, CES will be in an all-digital format for the first time.

So how can a virtual show like CES create immersive experiences for attendees tuning in remotely? That’s an interesting challenge for the cloud, and of course, every challenge presents an opportunity.

Google Cloud and 5G help enterprises deliver new experiences

Earlier in 2020, we announced our enterprise telecommunications strategy to deliver workloads to the network edge on Google Cloud, and during our SearchOn event last October, we announced how cloud streaming technology can power augmented reality (AR) in consumer search results.

Now, we’re merging the best of both worlds: Technology built for consumer search can take advantage of our enterprise edge capabilities. In light of the COVID-19 pandemic, this past year accelerated our support for enhanced consumer experiences across the board in novel ways. For example, we attempted to address questions such as how potential buyers can make a purchasing decision when they can’t see the product up close. This question becomes even more critical when considering a large purchase such as a new car.

That’s exactly what Fiat Chrysler Automobiles (FCA) and Google Cloud are working together to solve. As part of FCA’s Virtual Showroom CES event, you can experience the new innovative 2021 Jeep Wrangler 4xe by scanning a QR code with your phone. You can then see an Augmented Reality (AR) model of the Wrangler right in front of you—conveniently in your own driveway or in any open space. Check out what the car looks like from any angle, in different colors, and even step inside to see the interior with incredible details.

“As we continue our journey towards becoming a customer-centric mobility company, FCA is adopting emerging technologies that enable us to accelerate and deliver at the speed of our customers’ expectations,” said Mamatha Chamarthi, Chief Information Officer, FCA – North America and Asia Pacific. “Through our collaborative partnership with Google, we are able to expand our efforts to provide an immersive customer experience.”

Harnessing the power of edge with 5G

In order to create a mixed-reality experience with a 3D car model, computer-aided design (CAD)-based data sources that represent a 3D vehicle with highly detailed geometry, depth, texture, and lighting were used. High-fidelity models, such as cars with full interiors, often mean large files (GBs in size). Traditionally, depending on your connection, this can result in long waiting times as assets are downloaded onto your phone. In addition, while mobile phones are more powerful than the Apollo Guidance Computer, they are no match for the power we have in the cloud. We still want to bring these high end experiences to everyone, regardless of their respective device or geographical location.

We solve this problem by rendering the model in Google Cloud, then streaming it to the devices.

Specifically, the Cloud AR tech uses a combination of edge computing and AR technology to offload the computing power needed to display large 3D files, rendered by Unreal Engine, and stream them down to AR-enabled devices using Google’s Scene Viewer. Using powerful rendering servers with gaming console grade GPUs, memory, and processors located geographically near the user, we’re able to deliver a powerful but low friction, low latency experience. This rendering hardware allows us to load models with tens of millions of triangles and textures up to 4k, allowing the content we serve to be orders of magnitude larger than what’s served on mobile devices (i.e., on-device rendered assets). Doing so leverages high-speed 5G connectivity and streams directly from Google Cloud’s distributed edge, delivering a rich, photorealist immersive experience. Customers like FCA benefit from Google’s years of investment and expertise in streaming technology (have you tried playing Cyberpunk2077 on Stadia yet?). With the expansion of 5G networks, not only will streaming enable the experience for anyone anywhere, but it will also cut the wait time of downloading large assets required for detailed AR/VR experiences, ultimately providing instant gratification.

Applications and experiences are at the core of a winning edge proposition

We’re working to make these capabilities available to all enterprise customers to enable innovative use cases such as leveraging AR to help design teams collaborate, technicians perform machine diagnostics, creating next-generation, live video experiences for sports events, enabling new customer experiences across many industries, and supporting our customers in their digital transformation. Stay tuned!

To see the Jeep Wrangler 4xe come to life whether you’re attending CES or not, scan the QR code below, or check out the FCA CES website. Depending on your OS, device, and network strength, you will see either a photorealistic, cloud-streamed AR model or an on-device 3D car model, both of which can then be placed in your physical environment.