Founded in 2006, PaGaLGuY started as a forum that enabled students, typically aged between 20 and 30 to discuss and seek advice on academic issues. By 2011, PaGaLGuY had increased its traffic to about 250,000 page views per month. The business is now one of India’s largest education networks and provides an app that users can download to their Android and iOS devices.

Over the past six years, PaGaLGuY has extended its service to include video advice from experts on education topics and grown the number of views of its pages to 1.5 million per day. As Head of Technology for the business, Sandeep Kalidindi has played a key role in ensuring PaGaLGuY is as engaging as possible to users. “Because the product is advertising based, the greater the user engagement, the greater the advertising revenue,” Kalidindi explains.

Google Cloud Platform Results

• Supported growth to 1.5 million page views per day and demand spikes that see requests increase from about 90 per second to about 1,200 per second
• Reduced API latency from about 1 second to about 40 milliseconds
• Reduced system administration time from three to four days per week to 30 minutes every two weeks

In 2015, PaGaLGuY’s senior management team decided to deliver an even more relevant experience for users of the education network. “The core thing we had to do was personalise the experience for each and every student that visited PaGaLGuY,” Kalidindi says. “So we had to capture each student’s data to customise what they see when they open the site.”

The business also found traffic to the network was straining its infrastructure. During demand peaks, created by exams involving as many as 5 million students, PaGaLGuY would be inaccessible for periods of 30 minutes to one hour. Furthermore, average API latency had climbed to an unacceptable 1 second, compromising performance.

PaGaLGuY needed to access extensive compute resources to undertake its planned change. Had the business relied on a physical technology architecture to undertake the transformation, it would have had to purchase capacity equivalent to 16 new servers. “There was no way with a small team we could grow to that extent in a short time,” Kalidindi says. “This was the right moment for us to explore cloud services.”

The business established two primary requirements the selected cloud service needed to meet. First, PaGaLGuY had to be able to scale the platform with costs rising only in proportion to the increase in resources consumed. Accordingly, the business would have to minimise the number of employees required to manage the cloud environment. Second, the platform had to give PaGaLGuY easy access to student data and the ability to undertake prompt, granular analysis.

PaGaLGuY reviewed available public cloud services and determined that Google Cloud Platform (GCP) was the best fit for its business. “Google Cloud Platform was considerably more mature than the alternatives, with a high degree of automation and a suite of managed services,” Kalidindi says. PaGaLGuY management then discussed with Google how to optimise cost, performance and availability of its personalised education network on GCP.

With assistance from Google and business transformation specialists Searce, PaGaLGuY was able to deliver the platform into production on GCP in 10 months. “Searce was very proactive in ensuring the environment met our needs and allowing us to gain priority access to Google services in development,” Kalidindi says. “Their team was integral to the success of the migration.”

PaGaLGuY has been running in production in GCP for two years. The education network’s GCP architecture comprises a scalable back-end built on Google App Engine; a managed environment for its containerised applications in Google Kubernetes Engine; messaging-oriented middleware through Google Cloud Pub/Sub; a relational database in Google Cloud SQL; a managed data analytics warehouse running in Google BigQuery; stream and batch data processing through Google Cloud Dataflow; and object storage in Google Cloud Storage.

PaGaLGuY has leveraged GCP services to break down its platform application from a monolithic build to a series of microservices running in Google App Engine that enable independent deployment cycles, minimise test and quality assurance overheads and provide clearer monitoring and logging.

Running on GCP has enabled PaGaLGuY to add new personalisation features and grow fourfold without having to add any new engineers or administrators to accommodate the increased traffic. The business has also used the platform to seamlessly collect and aggregate students’ data for analysis, reporting and delivering a more targeted user experience. Furthermore, PaGaLGuY has been able to provide its management team with direct access to Google BigQuery to scrutinise data rather than require them to wait at least a day to view reports created by the product or technology teams.

Support demand peaks of 1,200 requests per second

“Thanks to Google Cloud Platform, we can easily support demand peaks that see requests per second rise from an average 90 per second to about 1,200 per second for as long as 45 minutes,” Kalidindi says. Due to GCP’s scalability, PaGaLGuY can ensure its education network remains available and performance remains consistent during those periods.

Latency cut to 40 milliseconds

The business has also reduced average API latency from 1 second to about 40 milliseconds. Furthermore, using GCP has enabled PaGaLGuY to automate most of its processes and reduce system administration requirements from three to four days a week across its team members to about half an hour per week.

The performance of GCP has transformed PaGaLGuY’s culture and processes. “Once our team was exposed to Google Cloud Platform and understood the superiority of the platform, our mindset changed from ‘let us do everything on our own’ to ‘let us do what we do best’ and delegate the remainder,” Kalidindi says. The quality of the service provided by GCP means PaGaLGuY effectively considers the cloud provider as part of its team. “We are always eager to see what new services are being launched and are extremely excited about what Google Cloud Platform can provide as part of its roadmap.” he concludes.

Today, we’re announcing that we’re working with AT&T to establish Messages by Google as the default messaging application for all AT&T customers in the United States using Android phones. The collaboration aims to help accelerate the industry toward global Rich Communication Services (RCS) coverage and interoperability to offer a consistent, secure, and enhanced messaging experience for all Android users around the world.

“Many AT&T customers have enjoyed the advantages of RCS for years when texting with friends and family,” said David Christopher, executive vice president and general manager – AT&T Mobility. “We look forward to working closely with Google to extend these benefits to even more of our customers as they enjoy richer conversations with others around the world.”

Working together, AT&T and Google will continue the momentum to upgrade SMS with enhanced messaging features offered in Messages, which includes the support of chat features based on the open RCS standard. With Messages as the default messaging application, all AT&T customers using Android devices will get enhanced features so they can:

• Share full-resolution pictures from a recent event or vacation 
• Send a higher-quality video of that soccer goal and the celebration that followed
• Know when someone is replying to a text
• Send and receive messages over Wi-Fi or data
• Participate in group chats where it’s easy to add someone else to the conversation, or let someone leave, without starting a brand new thread

In addition to these features, we’re also rolling out end-to-end encryption for one-on-one RCS conversations between people using Messages and people who have chat features enabled.

For years, we’ve been working with the mobile industry and device makers to bring enhanced and secure messaging to everyone on Android. Today’s announcement—that AT&T customers using Android devices will soon be able to enjoy these features by default—is a major step forward.

Cloud developers — you’ve got it all. You can code in your choice of languages, enjoy portability with containers, minimize complexity with serverless, and manage the entire software lifecycle by following DevOps principles. But let’s face it, building and onboarding new cloud applications still requires a lot of manual planning, synthesis, and, yes, hard work. You need to research and plan your deployment, create a workable, secure architecture, and of course, you need to write the actual code,

For the last several decades, the cloud has been primarily a “do it yourself” model with volumes of options that have made development more complicated. The cloud went from overwhelmingly exciting to…. a bit overwhelming.

What if we all could bring that excitement back? What if you had some help that was available whenever and wherever you needed it? 

Say hello to Duet AI for Google Cloud

Powered by Google’s state-of-the-art generative-AI foundation models, Duet AI for Google Cloud is an always-on AI collaborator that provides help to users of all skill levels where they need it. With Duet AI, we’re on a mission to deliver a new cloud experience that’s personalized and intent-driven, and can deeply understand your environment to assist you in building secure, scalable applications, while providing expert guidance. 

As we evolve Google Cloud with Duet AI, we are looking to build a cloud platform that is more human-centric, holistic, and helpful, with responsible AI at the center of the experience:

• Human-centric: With Duet AI, we are making Google Cloud more accessible and personal to any type of user at any skill level by providing them with support whenever they need it, from code recommendations for developers, to prompt-based data insights for data engineers, to chat-based app creation for business users.
• Holistic: With generative AI at the center of the cloud experience, cloud development can be more cohesive, with fewer silos across functions, services, and tech stacks, providing a holistic picture in the format you want, wherever you are in Google Cloud.
• Helpful: To deliver smarter, contextual recommendations for building and operating apps with Google Cloud, we pre-trained Codey, one of the foundation models that powers Duet AI, with Google Cloud-specific content like documentation and sample code, and fine-tuned it based on Google Cloud  user behaviors and patterns. 
• Responsible: Our AI Principles set out our commitment to developing technology responsibly. Your code and recommendations will not be reused for any model learning and development. This helps ensure the privacy of your data and code, and also the integrity of the knowledge space from which our AI models are trained.

New capabilities available in Duet AI for Google Cloud

Here are some of the new capabilities available to get us started on our mission to deliver a new personalized and intent-driven cloud experience:

• Code assistance provides AI-driven code assistance for cloud users such as application developers and data engineers. It gives code recommendations as they type in real time, generates full functions and code blocks, and identifies vulnerabilities and errors in the code, while suggesting fixes.

Code assistance auto-generates code for creating a Google Cloud Storage bucket

Code assistance will be available through multiple products and services across Google Cloud, such as in Cloud Workstations, our fully-managed secure development environment, and other code-editing experiences in the Google Cloud Console. Developers will also find code assistance in Cloud Shell Editor or via our Cloud Code IDE extensions for VSCode and JetBrains IDEs. It supports multiple languages including Go, Java, Javascript, Python, and SQL.

• Chat assistance allows people to use simple natural language to get answers on specific development or cloud-related questions. Users can engage with chat assistance to get real-time guidance on various topics, such as how to use certain cloud services or functions, or get detailed implementation plans for their cloud projects. It can also provide architectural or coding best practices, helping to reduce the need to go searching for relevant documents.

Use chat assistance to get the detailed steps for deploying an app on Cloud Run

Chat assistance will also be available across multiple Google Cloud surface areas, for example IDEs, the Cloud Console, and through products and services. Whether you’re a developer, operator, data engineer, or security professional, you’ll be able to leverage chat assistance to help get more work done faster. 

Looking to optimize these features further for developers specialized in one particular area? With Generative AI support in Vertex AI, enterprises can fine-tune Codey using their own code base. They can consume these customized Codey models directly from Vertex AI today, and later this year, they will be able to connect it to the built-in Duet AI experience. And don’t worry, if you choose to train Codey with your code, your private data is kept private, and not used in the broader foundation model training corpus. You will have transparency and control over where data is stored and how or if it is used. 

• Duet AI for AppSheet will let users create intelligent business applications,  connect their data, and build workflows into Google Workspace via natural language.  With no coding required, users will be able to build apps by describing their needs in a chat guided by AI-powered prompts. This makes app creation accessible to more users, which can allow developer teams to focus their time on other high-impact work.

Create business applications with Duet AI for AppSheet using natural language

Experiment with Duet AI for Google Cloud today

We believe that having an assistant who is constantly evolving by your side will not only reduce an already overwhelmed developer’s workload, but also bring back the excitement of cloud development. With Duet AI, you can navigate the cloud with more confidence, ease, and — dare we say it — fun. 

And this is just the beginning. The future of the cloud experience that we are shaping with Duet AI is full of possibilities. We believe the future of developer productivity is more targeted personalized assistance. Check here to see our vision for Duet AI for Google Cloud – the redefinition of productivity in the workplace through unique end-to-end AI assisted technologies. 

These early features of Duet AI for Google Cloud are available today for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.

MIXI, Inc. (MIXI) is a social networking organization that provides a diverse range of services for friends and family to enjoy together, such as the social-media platform mixi, a mobile game called Monster Strike, and a family photo and video sharing service known as FamilyAlbum. One of our current projects is Romi, a social robot launched in April 2021 that uses Speech-to-Text by Google Cloud as its speech recognition engine.

Since the late 2010s, the social robot market has been booming, with some models becoming increasingly affordable for consumers, from robotic tutors that promote social and cognitive development for children, to companion robots for elderly care. But with Romi, there is a marked difference in the quality of dialogue that makes Romi distinct from most social robots. 

The biggest feature of Romi is that the AI developed internally by MIXI can generate natural exchange of communication. The size of a hand-held device, Romi can be placed anywhere in a room and has a screen to demonstrate different facial expressions. It responds to conversation within context. Until now, AI has been used to interpret the intentions behind user speech, but Romi is an AI-powered robot that takes it a step further, generating spoken conversations. After all, Romi was created to offer heartwarming communication to those who are looking for it. This form of speech recognition did not exist before Romi was released. We hope users will enjoy conversing with it, including the occasional unexpected response.

The speech recognition part was one of the most critical aspects of Romi. Most of the infrastructure that makes up Romi uses a main public cloud, which was used for other services then. As for speech recognition, we decided to try out the Speech-to-Text tool by Google Cloud, which was praised for its overwhelmingly high accuracy, and the prototype’s results were very positive. Even though we tried other companies’ services before making the final decision, our conclusion about Speech-to-Text remains the same. 

The accuracy and responsiveness of Speech-to-Text made the tool an effective one for a social robot like Romi. Google Cloud also provided a sense of security with its high reliability that has been demonstrated in enabling Romi’s workloads, and will be able to support continuous development of Romi’s services for the long run.

With the rapid development of speech recognition technology, MIXI decided to re-examine the speech recognition engine for Romi in June 2022, about a year after its release. We eventually decided to continue its use of Speech-to-Text. We reviewed about 10 companies’ Japanese-compatible speech recognition engines, and found that Speech-to-Text offered the best results. In addition, Speech-to-Text has several speech recognition transcription models, but we found that the latest short model, which specializes in short utterances, is more suitable for Romi than the default model.

The cost-savings that Speech-to-Text delivers is also impressive. The billing unit was changed from 15 seconds increments rounded up, to one second in November, and huge cost reductions could be expected with Romi. This is important to us because Romi does not have trigger phrases, such as “OK Google,” so as to achieve more natural conversations. As a result, it can recognize and process more speech as compared to other social robots. While this results in a more user-friendly experience, it also requires greater workloads and can incur a higher cost compared to most speech recognition engines. But with the updated billing system that Speech-to-Text delivers, we are able to continue refining Romi’s speech recognition accuracy while keeping costs low. 

Improving data analysis with BigQuery

Google Cloud was only used for speech recognition initially, but as Romi’s range of service expanded, more aspects of Romi were hosted on Google Cloud. Among these features, the machine learning platform for AI was moved to Google Cloud at an early stage. To be able to make use of a cloud platform at an affordable cost makes Google Cloud very appealing. Premium Support and technical account management helped us with our cost considerations.

Furthermore, MIXI started migrating the data analysis platform for Romi to BigQuery last year. BigQuery was chosen because it excels at bringing together and analyzing big data in various formats, as in-depth data analysis becomes necessary to improve Romi’s services. What also makes BigQuery an attractive choice was the ability to introduce structured query language (SQL) to BigQuery, a language that the development team from MIXI is familiar with. 

In particular, we are grateful for the use of software like Looker. It takes a lot of work, even for engineers, to write complex queries, but with Looker, even non-engineers can intuitively perform fairly complex analysis. About half a year ago, we held regular briefings mainly for employees interested in data analysis, and now they voluntarily conduct analysis, conduct discussions based on the results, and create new projects and ideas. This has become a regular workflow for us.

Currently, what is popular in AI-based communication is the emergence of large-scale language models (LLMs) that learn from huge amounts of data, and generate natural responses on a different level than before. 

To improve the conversational experience with Romi, we have been looking into relevant LLM technologies for a while now. It is important to be able to use high performance GPUs as inexpensively as possible in order to run PoC at high speed. We will continue to focus on Google Cloud services, including Compute Engine and VertexAI.