Whether they’re reading the newspaper on the way to work, or catching up on the latest headlines on their smartphones, readers expect up-to-the-minute news wherever and whenever makes the most sense for them. As a result, media companies are increasingly looking for ways to improve, expand, and simplify their offerings, and they’re increasingly looking to the cloud to do it.

For more than 160 years The Telegraph has been counted on by readers across the United Kingdom and globally for award-winning news and journalism. An early adopter of cloud technology, it’s been a G Suite customer since 2008 and has already been using Google Cloud Platform to analyze digital behaviors to improve engagement and advertising performance since 2016.

Recently, The Telegraph announced it’s migrating fully to Google Cloud. By migrating all their production and pre-production services, they aim to deliver content faster, provide compelling experiences to readers, and reduce environmental impact.

“We are delighted to announce our newest collaboration with Google Cloud,” said Chris Taylor, Chief Information Officer, The Telegraph. “We have always worked closely with Google as they help us to provide our readers with great experiences on our digital products, collaboration software and internet scale through search. Their continued leadership in projects such as Kubernetes are enabling us to build flexible development environments that truly support DevOps.”

Powering the Digital Publishing Ecosystem

The Telegraph produces large volumes of digital content every day. It was imperative for them to find a cloud provider they could trust to support this ecosystem. By working with Google Cloud they have changed the way they see and engage with data: they can collect new information about their products every second and use that to continually hone their strategy. The Telegraph are placing more confidence and trust in the data captured about their content and now have one of the best available pieces of technology for capturing and analyzing the stories they publish in real-time.

Leveraging AI to support journalists

Time is critical when journalists are on a story, and The Telegraph wants to put important data in the hands of its journalists right when they need it. To do this, it will be using AutoML to classify content for journalists and make it more discoverable. For example, a reporter will be able to bring up relevant assets that link to their stories. It will also apply AutoML to classify Telegraph stock photos to help journalists attach compelling visual content to their stories faster.

Building compelling reader experiences with the help of APIs

Readers have an ever-increasing expectation of personalization. To meet this need, The Telegraph launched My Telegraph, currently live in beta, to offer registered readers personalized news experiences based on their interests or the particular journalists they want to follow. My Telegraph was developed on an API management platform provided by Google Cloud’s Apigee. You can learn more about how it’s applying API management to My Telegraph, in this blog post.

Working for environmental good

The Telegraph is the biggest selling quality newspaper in the UK, an accolade which requires it to print and distribute hundreds of thousands of copies each day. Optimal management of print production is important, and by using a combination of the cloud and machine learning, The Telegraph is better able to predict demand for physical newspapers, maximizing sales and minimizing waste. This makes great business sense for The Telegraph but also has great environmental benefit.

At Google Cloud, we’re committed to making artificial intelligence (AI) accessible to everyone and easier to harness for new use cases.  That’s why we’re excited to announce the general availability of our intuitive, new visual user interface for Google Cloud’s Speech-to-Text (STT) API, right in Google Cloud Console, which makes the API much simpler and easier for developers to use.  

The STT API lets developers convert speech into text by leveraging Google’s years of research in automatic speech recognition and transcription technology. As advancements in AI continue to bring speech to new interfaces and devices, the STT API helps developers add speech functionality to their applications in order to better meet consumer demands. 

The STT API covers a wide variety of use cases, from dictation and short commands, to captioning and subtitles. Getting the most of STT, however, can be a complicated process. To achieve the highest accuracy on any AI use case requires careful testing and tuning. 

Previously, developers building on the STT API had to do this work manually by carefully experimenting with our API. Just to get started, developers needed familiarity with GCP integration concepts and had to either build their own tools or manage various scripts and API calls to fully understand the API documentation.  These actions required cumbersome and time-consuming effort and made measuring, customizing, and improving models even more difficult.  

Today’s announcement significantly simplifies the process, facilitating iteration and integration of models into developers’ applications by letting developers perform every API function from within the Google Cloud Console.  These tools will make it easier for developers to integrate the STT API with their products or services. This update also gives developers the ability to manage and quickly iterate on their STT model customizations with Model Adaptation.

Model Adaptation allows developers to customize STT specifically for their domains or use cases. Developers can maintain lists of words and weights that will be applied to either every request or just single requests, depending on their needs. Model adaptations are reusable and composable, so once developers have seen good results in the STT Cloud Console, they can deploy to their entire solution. 

The Speech-to-Text Cloud Console and Model Adaptation API is available now in all Google Cloud regions and languages and is accessible to all GCP users with no additional cost to that of the underlying API usage. The STT API supports over 70 languages in 120 different local variants. If you’re a developer looking for an easy to use, easy to integrate, and high-quality STT experience, sign up for our free trial and try our new interface on your own datasets today!

The name Kitabisa means “we can” in Bahasa Indonesia, the official language of Indonesia, and captures our aspirational ethos as Indonesia’s most popular fundraising platform. Since 2013, Kitabisa has been collecting donations in times of crisis and natural disasters to help millions in need. Pursuing our mission of “channeling kindness at scale,” we deploy AI algorithms to foster Southeast Asia’s philanthropic spirit with simplicity and transparency.

Unlike e-commerce platforms that can predict spikes in demand, such as during Black Friday, Kitabisa’s mission of raising funds when disasters like earthquakes strike is by definition unpredictable. This is why the ability to scale up and down seamlessly is critical to our social enterprise.

In 2020, Indonesia’s COVID-19 outbreak coincided with Ramadan. Even in normal times, this is a peak period, as the holy month inspires charitable activity. But during the pandemic, the crush of donations pushed our system beyond the breaking point. Our platform went down for a few minutes just as Indonesia’s giving spirit was at its height, creating frustrations for users.

A new cloud beginning

That’s when we realized we needed to embark on a new cloud journey, moving from our monolithic system to one based on microservices. This would enable us to scale up for surges in demand, but also scale down when a wave of giving subsides. We also needed a more flexible database that would allow us to ingest and process the vast amounts of data that flood into our system in times of crisis.

These requirements led us to re-architect our entire platform on Google Cloud. Guided by a proactive Google Cloud team, we migrated to Google Kubernetes Engine (GKE) for our overall containerized computing infrastructure, and from Amazon RDS to Cloud SQL for MySQL and PostgreSQL, for our managed database services.

The result surpassed our expectations. During the following year’s Ramadan season, we gained a 50% boost in computing resources to easily handle escalating crowdfunding demands on our system. This was thanks to both the seamless scaling of GKE and recommendations from the Google Cloud Partnership team on deploying and optimizing Cloud SQL instances with ProxySQL to optimize our managed database instances.

A progressive journey to kindness at scale

While Kitabisa’s mission has never wavered, our journey to optimized performance took us through several stages before we ultimately landed on our current architecture on Google Cloud.

Origins on a monolithic provider
Kitabisa was initially hosted on DigitalOcean, which only allowed us to run monolithic applications based on virtual machines (VMs) and a stateful managed database. This meant manually adding one VM at a time, which led to challenges in scaling up VMs and core memory when a disaster triggered a spike in donations.

Conversely, when a fundraising cycle was complete, we could not scale down automatically from the high specs of manually provisioned VMs, which was a strain on manpower and budgetary resources.

Transition to containers
To improve scalability, Kitabisa migrated from DigitalOcean to Amazon Web Services (AWS), where we hoped deploying load balancers would provide sufficient automated scaling to meet our network needs. However, we still found manual configurations to be too costly and labor-intensive.

We then attempted to improve automation by switching to a microservices-based architecture. But on Amazon Elastic Container Service (Amazon ECS) we hit a new pain point: when launching applications, we needed to ensure that they were compatible with CloudFormation in deployment, which reduced the flexibility of our solution building due to vendor locking.

We decided it was “never too late” to migrate to Kubernetes, which is a more agile containerized solution. Given that we were already using AWS, it seemed natural to move our microservices to Amazon Elastics Kubernetes Service (Amazon EKS). But we soon found that provisioning Kubernetes clusters with EKS was still a manual process that required a lot of configuration work for every deployment.

Unlocking automated scalability
At the height of the COVID-19 crisis, faced with mounting demands on our system, we decided it was time to give Google Kubernetes Engine (GKE) a try. Since Kubernetes is a Google-designed solution, it seemed likeliest that GKE would provide the most flexible microservices deployment, alongside better access to new features.

Through a direct comparison with AWS, we discovered that everything from provisioning Kubernetes clusters to deploying new applications became fully automated, with the latest upgrades and minimal manual setups. By switching to GKE, we can now absorb any unexpected surge in donations, and add new services without expanding the size of our engineering team. The transformative value of GKE became apparent when severe flooding hit Sumatra in November 2021, affecting 25,000 people. Our system easily handled the 30% spike in donations.

Moving to Cloud SQL and ProxySQL

Kitabisa was also held back by its monolithic database system, which was prone to crashing under heavy demand. We started to solve the problem by moving from a stateful DigitalOcean database to a stateless Redis one, which freed us from relying on a single server, giving us better agility and scale.

But the strategy left a major pain point because it still required us to self-manage databases. In addition, we were experiencing high database egress costs due to the need to execute data transfers from a non-Google Cloud database into BigQuery.

In December 2021, we migrated our Amazon RDS to Cloud SQL for MySQL, and immediately saved 10% in egress costs per month. But one of the greatest benefits came when the Google Cloud team recommended using the open source proxy for MySQL to improve the scalability and stability of our data pipelines.

Cloud SQL’s compatibility allowed us to use connection pooling tools such as ProxySQL to better load balance our application. Historically, creating a direct connection to a monolithic database was a single point of failure that could end up in a crash. With Cloud SQL plus ProxySQL, we create layers in front of our database instances. It serves as a load balancer that allows us to connect simultaneously to multiple database instances, by creating a primary and a read replica instance. Now, whenever we have a read query, we redirect the query to our read replica instance instead of the primary instance.

This configuration has transformed the stability of our database environment because we can have multiple database instances running at the same time, with the load distributed across all instances. Since switching to Cloud SQL as our managed database, and using ProxySQL, we have experienced zero downtime on our fundraising platform even when a major crisis hits.

We are also saving costs. Rather than having a separate database for each different Kubernetes cluster, we’ve merged multiple database instances into one instance. We now group databases according to business units instead of per service, yielding database cost reductions of 30%.

Streamlining with Terraform deployment

There’s another key way in which Google Cloud managed services have allowed us to optimize our environment: using Terraform as an infrastructure-as-a-code tool to create new applications and upgrades to our platform.

We also managed to automate the deployment of Terraform code into Google Cloud with the help of Cloud Build, and no human intervention. That means our development team can focus on creative tasks, while Cloud Build deploys a continuous stream of new features to Kitabisa.

The combination of seamless scalability, resilient data pipelines, and creative freedom is enabling us to drive the future of our platform, expanding our mission to inspire people to create a kinder world in other Asian regions.

We believe that having Google Cloud as our infrastructure backbone will be a critical part of our future development, which will include adding exciting new insurtech features. Now firmly established on Google Cloud, we can go further in shaping the future of fundraising to overcome turbulent times.

Financial markets were among the first to adopt new technologies, and that has certainly been true of the derivatives markets, which were early adopters of electronic trading. Going forward, new capabilities will transform the way industry participants communicate, analyze, and trade.

I sat down with Google Cloud’s Phil Moyer and former SEC Commissioner, Troy Paredes, for a fireside chat at FIA Boca 2022 to discuss the future of markets and policy, the new technologies that are already paving the way for greater speed and transparency, and how cloud can help promote greater resiliency, performance, and security to enable the long-term vision for the market. The following is a summary of our discussion.

The current state of cloud technology

When it comes to technology adoption, we’re seeing the market and participants adopt cloud technologies, and increasingly, machine learning (ML) on a wider scale. Cloud technology allows for easier, faster, and much more secure experimentation with large datasets and ML.

A recent Google sponsored study by Coalition Greenwich (September, 2021) showed that more than 93% of trading systems, exchanges, and data providers are in some way providing services on the cloud. The same study, revealed that about 72% of the financial industry across the buy side and sell side, intend to consume public cloud-data based market data within the next 12 months.

Data-driven decision-making and risk management have always been, and continue to remain, the cornerstones of the financial markets. Over time, technology innovation has facilitated access to better insights from data, and therefore, better decision-making and the ability to manage risk. That expectation is now mainstream, and will continue to grow in sophistication.

The multi-phased technology trajectory

The movement of exchanges to the cloud will occur in a “crawl-walk-run” fashion, with low-hanging fruits the first to be picked in the near term while bigger, paradigmatic changes will occur over the medium and long term. Some organizations are starting all three stages simultaneously, understanding that each will move at an independent cadence.

The “crawl” phase is one in which foundations are built, starting with organizations moving data to the cloud and experimenting with some degree of analytics. It’s one of the most important phases because it’s where the opportunity to increase transparency and risk management takes shape.

In moving to the cloud, the infrastructure – which in the past relied on a combination of people, processes, and some technology – becomes the code that runs applications. This early phase is key to empowering organizations to shift to a cloud-based, agile-first operating model that makes it easier and more seamless to launch new products in the future, including by freeing up people and resources from IT management to more mission-focused work.

Establishing the cloud operating model simplifies the “walk” and “run” phases where compliance is more automated, latency-sensitive applications are more readily available, and the next generation of exchanges, market participants, and regulators is better prepared to meet future challenges.

The “walk” phase is where much of the innovation happens. Exchanges are making significant progress in leveraging foundational data decisions in the “crawl” phase and innovations in the cloud to improve settlement, clearing, risk management, collateral management, and compliance, and launch new products.

And finally, the “run” phase is where organizations will start to move the latency-sensitive markets to the cloud, as the markets increasingly will demand low-latency and high performance along with transparency and analytics to solve historical obstacles to market access.

Opportunities for both regulators and market participants

Any time significant technological change takes place, regulators explore its implications, particularly with respect to their ability to meet their regulatory objectives.

Increasingly, we are seeing technological change driving more opportunities for regulators and market participants alike. Such changes may also allow better protection of the marketplace, with greater integrity and transparency.

Over time, regulatory regimes – rules, regulations, statutes, interpretations, and guidance – will also adjust to new technologies, both benefiting the marketplace and advancing regulatory goals.

As one example, the cloud is increasing the ability to meet compliance obligations by allowing compliance to be built into transactions. Moreover, predicated on the vision of real-time regulatory reporting, and given the pace of technological change in the marketplace over the last several years, various regulators have been using more advanced analytics. This trend will continue to help them more effectively and efficiently meet their objectives, and monitor and meet the expectations they have for the entire market.

Machine learning’s role in the financial markets

Google Cloud’s head of AI and Industry Solutions, Andrew Moore, said that ML will be doing three key things for us in the next 10 years: giving us meaning, providing concierge services, and serving as a guardian. Extracting information that is critical to investor decision-making can be extremely important. With more data than ever, ML can increase the ability to process it while also becoming more accessible in the cloud and better supporting regulatory objectives.

The technology will likely manifest in trading and anti-money laundering activities as they relate market functions, as well as managing a wide variety of risks – supporting the interests of both investors and regulators in terms of decision-making, surveillance, and protections.

Rather than taking individuals out of the equation, the digitization of markets, assets, and guard rails combined with ML will allow people to focus their expertise in different ways to achieve key objectives.

Building the market foundation for the future

The goals of operational resiliency, security, and privacy will continue to be critical for building the market foundation for both participants and regulators. While technology promises to create advantages in concrete, tangible ways, it will be important to scrutinize potential risks and concerns.

Priority one for technology providers is to build an environment of trustless security, including encryption at motion and encryption at rest, ensuring that markets are operationally resilient while instilling confidence for any exchange that runs on top of that infrastructure. Multicloud architectures and approaches are likely also to be part of the solution for operational resilience.

Throughout time, liquidity has been the outcome of improved access, transparency, and security. Technology providers are responding by sharing both the responsibility for, and fate of, the markets of the future to build an efficient, faster, and more transparent and secure financial industry.

You can learn more about our approach in our newest white paper, Building the financial markets foundation for the future.