While some traditional financial services companies have more slowly transitioned to the cloud, capital markets firms have embraced cloud computing across their entire value chains — front-, middle-, and back-office. We wanted to understand the dynamics behind this rapid adoption, the most common use cases, and the types of technology most in use, particularly as it relates to market data. Google Cloud commissioned Coalition Greenwich to survey 102 institutional capital markets professionals — at exchanges, trading systems, data aggregators, data producers, asset managers, hedge funds, and investment banks — in the United States, Canada, France, Germany, Italy, the Netherlands, Switzerland, and the United Kingdom. 

Our research found that while there are many drivers, demand for easier accessibility is fueling widespread adoption of cloud-based market data services, and associated trading infrastructures, across the buy side and sell side. In fact, 68% of sell-side and buy-side users find it critical for market data providers to offer public cloud-based data services. At the same time, exchanges, market data providers, aggregators, and trading systems are embracing the cloud as a delivery model by offering access to data directly via their own cloud services, APIs or partners.

Here were five noteworthy takeaways from the study: 

1. Cloud services are becoming ubiquitous for data delivery. Today, the cloud is pervasive, with 93% of exchanges, trading systems and data providers offering cloud-based data and services, according to surveyed executives. Moreover, 100% of those surveyed intend to offer new cloud-based services, such as derived data, in the next 12 months.

2. Commercial and investment banks are offering additional connectivity, real-time data feeds, and trading applications delivered via the cloud,demonstrating that it’s not only exchanges, trading systems, and data providers that are moving rapidly to the cloud. Internal use cases abound as well, with 67% of those surveyed consuming cloud-deployed market data, primarily for data analytics. 88% of surveyed sell-side firms intend to consume cloud-based market data services, with digital transformation, data science and quant research as the top use cases.

3. Buy side firms will consume even more cloud-deployed data. Today, 90% of surveyed buy-side firms are consuming cloud-deployed market data, mostly for portfolio management. 70% of buy-side firms intend to consume more public cloud-based market data services in the next 12 months, adding services such as compliance and regulatory reporting.

4. AI/ML, powered by cloud, is moving out of the pilot phase and into mainstream use. Today, 50% of exchanges, trading systems, and data providers are offering data products or services powered by AI/ML, and of those, 42% intend to offer AI-powered trade execution and trading analytics services in the next 12 months. Within commercial and investment banks, 55% said they are currently using AI/ML in the cloud, and while that was true for only 14% of overall buy-side respondents, 44% of large buy-side respondents are using it.

5. Exchanges, trading systems, and data providers are prioritizing public cloud for internal insights. 71% of these firms are using the public cloud, mostly for data transmission, processing, analysis, and long-term data storage. Over the next 12 months, 33% of new public cloud workloads will focus on data mining, data insights and advanced analytics, while 28% of new AI/ML tooling and infrastructure investments will focus on faster analytics and risk reviews, and 27% on data quality maintenance.

“We see new, dramatic shifts on the adoption of cloud across market data,” said David Easthope, Senior Analyst for Coalition Greenwich. “And we expect further proliferation of cloud-based services and greater consumption across the trading and investing lifecycle.”

Conclusions and future predictions

Based on the survey results, Coalition Greenwich predicts five following trends over the next 12 months:

1. Exchanges and trading systems will continue to launch a wide array of new cloud-based and possibly cloud exclusive data services across derived data, end of day data, reference data and pricing data.
2. Data providers will launch new data products such as pre-trade analytics powered by AI/ML in the cloud.
3. Commercial and investment banks will offer additional connectivity, real-time data feeds, and trading applications delivered via the cloud.
4. Buy-side firms will consume even more cloud-deployed data, including real-time market data, portfolio management data, and risk analytics.
5. Exchanges, trading systems and data providers will explore proof-of-concepts around core systems on the cloud. Improvements to AI/ML tooling or infrastructure will ramp up as firms seek more rapid responses to risk initiatives.

To learn more about these findings, download our two full reports, The Future of market data: Distribution and consumption through cloud and AI and Exchanges and data providers: Prioritizing the cloud and AI for internal insights or our short infographic.

Research methodology

The survey was conducted online by Coalition Greenwich on behalf of Google Cloud from March 2021 to April 2021 among 102 executives in North America (n=82), EMEA (n=17) and other (n=3) who are employed full-time and who are participants or influencers in decisions around cloud and/or senior management with a role at a company which is an institutional asset manager, hedge fund, alternative investment manager, exchange and/or trading system, information provider, information aggregator, or other asset manager/asset owner. The survey included wide perspectives from a range of firm size and asset class focus, including equity, fixed income, FX, commodities, multi-asset, and other asset classes.

Foot Notes

1.  We defined market data as direct feeds, consolidated feeds, terminal and desktop products, security and reference data, pricing data, historical data, alternative data, and index data.

Volkswagen strives to design beautiful, performant, and energy efficient vehicles. This entails an iterative process where designers go through many design drafts, evaluating each, integrating the feedback, and refining.

For example, a vehicle’s drag coefficient—its resistance to air—is one of the most important factors of energy efficiency. Thus, getting estimates of the drag coefficient for several designs helps the designers experiment and converge toward more energy-efficient solutions. The cheaper and faster this feedback loop is, the more it enables the designers.

Unfortunately, estimating drag coefficient is an expensive and time-consuming operation that involves either a physical wind tunnel or a computationally intensive simulation. This can be a bottleneck in the feedback cycle.

For this reason, Volkswagen and Google Cloud decided to collaborate on a joint research project to investigate using machine learning (ML) to get fast and inexpensive estimates of the drag coefficient. In this post, we’ll explore the challenges and approaches undertaken in this project.

The core principles of the project were simple. First, we needed to collect a dataset of existing car designs and their respective drag coefficients. Then, we needed to create a representation of the various cars that would be suitable for ML. The next step was to train a deep learning model to predict the drag coefficient, and then, finally, we would use that model to efficiently estimate drag for any new design.

Representing three-dimensional car designs

Design software recreates a physical object as a three-dimensional triangle mesh made up of three types of objects—faces, edges, and vertices. Figure 1, below, shows such a mesh for an Audi S6. Faces are flat surfaces, such as the window in a car door. An edge is where two faces meet (e.g., the side of the door), and a vertex is where two or more edges meet, such as the corner of the door.

Car bodies, however, come in all shapes and sizes. A Volkswagen Golf economy model is very different from a Tiguan SUV, and a single vehicle can have both large smooth surfaces as well as areas with delicately designed features. Consequently, there can be a huge variety from one polygonal mesh to the next.

ML models need consistent representation in order to form robust generalized rules. With such a dramatic variance between each polygonal mesh, the models would be compromised and the results could have huge margins of error.

We needed to find a way to create simple meshes that capture the shape of the car but are still suited for ML models.

Representing a car with digital shrink wrapping

Rather than building a representation of each car from the ground up, we applied a “shrink wrapping” method for the 3D meshes. The principle is very similar to vacuum-sealing a cucumber. The cucumber is placed in a plastic bag and the air is then gradually removed until the bag fits tightly around it, capturing its shape.

Our approach works similarly: we start with a base mesh, a simple shape that corresponds to the plastic bag, and we deform it until it captures the shape of the target mesh. For our purposes, the base mesh is a simplified representation of a car and the target mesh is the particular car we are designing for at that moment. Such meshes can be defined, managed, and presented to ML models for training using the Tensorflow Graphics and trimesh libraries.

Our “shrink wrapping” method mainly works by iteratively minimizing a measure of distance (e.g., chamfer distance) between the two meshes. Additionally we can regularize our mesh to preserve certain qualities, like smoothness, in the resulting mesh. This iterative optimization is analogous to the vacuum pump, gradually shrinking and fitting the vertices of the mesh as closely as possible to the complex shape of the car. With shrink-wrapping, we are able to produce cleaner meshes that are more suitable to our estimation task. An example of such a procedure is shown in Figure 2.

How to train a model

Shrink-wrapping the 3D car designs was an important first step, but the work was far from over. Our next challenge was to build and test the machine learning algorithms.

We wanted our algorithms to estimate the drag coefficient as accurately and quickly as possible each time it looked at a new design. To do so, we had to train the ML models on existing data.

From publicly available datasets, we calculated the drag coefficients for 800 different car meshes, which we trained the models on. Then, we evaluated the trained models on a further 100 meshes, seeing how accurate their estimates were on new data.

As we worked through this training, we refined our approach. Initially, we tested models based on convolutional neural networks – similar to PointNet – that observed only the vertices, i.e., the fixed points in each mesh. But when we tested mesh-convolutional models – similar to FeastNet – we found a slightly different focus improved the accuracy of the estimates. Rather than focusing on vertices alone, these models looked at a mesh of vertices and how they relate to each other. These models placed each vertex in a richer context, leading to more accurate estimates when air-flow hit particularly subtle design features.

Working in parallel and at scale
To collaborate across time zones and two organizations, we’ve used the Google Cloud Vertex AI platform.

Vertex AI Workbench serves as a central hub to interact with other services and infrastructure on the Vertex AI platform. It enables quick experiments and preparation of training packages for resource-intensive ML model training jobs, all in a Python notebook environment for immediate execution of code. The notebook environments allow code-based interaction with other services on Google Cloud and ML tools such as Vertex AI Training and Vertex AI Pipelines.

The process of training a new model is a seamless one. First, a dataset is prepared and stored in Google Cloud Storage, usually with the help of Tensorflow Datasets. Then, for every ML model we want to test, we package and store the training code as a container image with Google Cloud Build and Container Registry. This ensures that every job is fully documented, including the provided parameters, training code package, logs from the training task, and resulting artifacts such as metrics and model files.

From there, we submit the model to the Vertex AI Training service, which provides easy access to large scale infrastructure and hardware accelerators, such as GPUs and TPUs, by simply defining resource needs when submitting a job. By using Vertex AI Training’s hyperparameter tuning feature, we can run experiments in parallel with multiple neural networks to find the right one for our purposes.

With Vertex AI Tensorboard, we can capture metrics and visualize the results of our experiments. These are readily available to anyone in the team, wherever they are in the world, for a wider discussion.

The first milestone

This joint research effort between Volkswagen and Google has produced promising results with the help of the Vertex AI platform. In this first milestone, the team was able to successfully bring recent AI research results a step closer to practical application for car design. This first iteration of the algorithm can produce a drag coefficient estimate with an average error of just 4%, within a second.

An average error of 4%, while not quite as accurate as a physical wind tunnel test, can be used to narrow a large selection of design candidates to a small shortlist. And given how quickly the estimates appear, we have made a substantial improvement on the existing methods that take days or weeks. With the algorithm that we have developed, designers can run more efficiency tests, submit more candidates, and iterate towards richer, more effective designs in just a small fraction of the time previously required.

Going forward, faster and more accurate estimates could even enable more automated searching for efficient designs, which would help both engineers and designers to hone in on the areas of the vehicle body where they could have the most impact. An important next step will be integrating the results into 3D design software to let designers benefit from the output and provide feedback.

As we continue, our focus is on improving the accuracy of the models. Firstly, we will build a larger, better quality dataset. Secondly, we will improve our shrink-wrapping algorithm to capture more details. Finally, we will enhance our existing models by experimenting with Vertex AI Neural Architecture Search to explore and experiment with different neural architecture options.

Moreover, we believe that our results for drag coefficient estimation is only a starting point for further exploration. There could potentially be numerous use cases in the space of physical simulations and assessments where cost and time savings could be achieved through ML-based estimators.

Acknowledgements

This work wouldn’t have been possible without the contributions from Volkswagen Data:Lab, Google Research, and Google Cloud. Thanks to Ahmed Ayyad, Dr. Andrii Kleshchonok, Dr. Daniel Weimer, Gülce Cesur, Henrik Bohlke, Andreas Müller from Volkswagen, Ameesh Makadia, Ph.D., and Carlos Esteves, Ph.D., from Google Research, and Daniel Holgate, Holger Speh, and Dr. Michael Menzel from Google Cloud.

Google Cloud’s Professional Services Organization (PSO) engages with customers to ensure effective and efficient operations in the cloud, from the time they begin considering how cloud can help them overcome their operational, business or technical challenges, to the time they’re looking to optimize their cloud workloads. 

We know that all parts of the cloud journey are important and can be complex.  In this blog post, we want to focus specifically on the migration process and how PSO engages in a myriad of activities to ensure a successful migration.

As a team of trusted technical advisors, PSO will approach migrations in three phases:

1. Pre-Migration Planning
2. Cutover Activities
3. Post-Migration Operations

While this post will not cover in detail all of the steps required for a migration, it will focus on how PSO engages in specific activities to meet customer objectives, manage risk, and deliver value.  We will discuss the assets, processes and tools that we leverage to ensure success.

Pre-Migration Planning

Assess Scope

Before the migration happens, you will need to understand and clarify the future state that you’re working towards.  From a logistical perspective, PSO will be helping you with capacity planning to ensure sufficient resources are available for your envisioned future state.

While migration into the cloud does allow you to eliminate many of the considerations for the physical, logistical, and financial concerns of traditional data centers and co-locations, it does not remove the need for active management of quotas, preparation for large migrations, and forecasting.  PSO will help you forecast your needs in advance and work with the capacity team to adjust quotas, manage resources, and ensure availability. 

Once the future state has been determined, PSO will also work with the product teams to determine any gaps in functionality.  PSO captures feature requests across Google Cloud services and makes sure they are understood, logged, tracked, and prioritized appropriately with the relevant product teams.  From there, they work closely with the customer to determine any interim workarounds that can be leveraged while waiting for the feature to land, as well as providing updates on the upcoming roadmap.  

Develop Migration Approach and Tooling

Within Google Cloud, we have a library of assets and tools we use to assist in the migration process.  We have seen these assets help us successfully complete migrations for other customers efficiently and effectively.

Based on the scoping requirements and tooling available to assist in the migration, PSO will help recommend a migration approach.  We understand that enterprises have specific needs; differing levels of complexity and scale; regulatory, operational, or organization challenges that will need to be factored into the migration.  PSO will help customers think through the different migration options and how all of the considerations will play out.

PSO will work with the customer team to determine the best migration approach for moving servers from on-prem to Google Cloud. PSO will walk customers through different migration approaches, such as refactoring, lift-shift, or new installs. From there, the customer can determine the best fit for their migration. PSO will provide guidance on best practices and use cases from other customers with similar use cases. 

Google offers a variety of cloud native tools that can assist with asset discovery, the migration itself, and post-migration optimization. PSO, as one example, will help work with project managers to determine the best tooling that accommodates the customer’s requirements for migrating servers. PSO will also engage Google product team to ensure the customer fully understands the capabilities of each tool and the best fit for the use case. Google understands from a tooling perspective, one size does not fit all, thus PSO will work with the customer on determining the best migration approach and tooling for different requirements. 

Cutover Activities

Once all of the planning activities have been completed, PSO will assist in making sure the cutover is successful.

During and leading up to critical customer events, PSO can provide proactive event management services which deliver increased support and readiness for key workloads.  Beyond having a solid architecture and infrastructure on the platform, support for this infrastructure is essential and TAMs will help ensure that there are additional resources to support and unblock the customer where challenges arise.

As part of event management activities, PSO liaises with the Google Cloud Support Organization to ensure quick remediation and high resilience for situations where challenges arise.  A war room is usually created to facilitate quick communication about the critical activities and roadblocks that arise.  These war rooms can give customers a direct line to the support and engineering teams that will triage and resolve their issues.

Post-Migration Activities

Once cutover is complete, PSO will continue to provide support in areas such incident management, capacity planning, continuous operational support, and optimization to ensure the customer is successful from start to finish.

PSO will serve as the liaison between the customer and Google engineers. If support cases need to be escalated, PSO will ensure the appropriate parties are involved and work to get the case resolved in a timely manner. Through operational rigor, PSO will work with the customer in determining if certain Google Cloud services will be beneficial to the customer objectives. If services will add value to the customer, PSO will help enable the services so it aligns with the customer’s goal and current cloud architecture. In cases where there are missing gaps in services, PSO will proactively work with the customer and Google engineering teams to close the gaps by enabling additional functionality in the services.  

PSO will continue to work with the engineering teams to consistently review and provide recommendations on the customer’s cloud architecture in ensuring the most optimal and cost efficient design along with adhering to Google’s best practices guidelines. 

Aside from migrations, PSO is also responsible for providing continuous training of Google Cloud to customers. To ensure consistent development of Google Cloud, PSO will work with the customer to jointly develop a learning roadmap to ensure the customer has the necessary skills to succeed in delivering successful projects in Google Cloud.

Conclusion

Google PSO will be actively engaged throughout the customer’s cloud journey to ensure the necessary guidance, methodology, and tools are presented to the customer. PSO will engage in a series of activities from pre-migration planning to post migration in key areas such as capacity planning to ensure sufficient resources are allocated for future workloads to providing support on technical cases for troubleshooting. PSO will serve as a long-term trusted advisor who will be the voice of the  customer and provide the reliability and stability of the customer’s Google Cloud environment.

Click here if you’d like to engage with our PSO team on your migration. Or, you can also get started with a free discovery and assessment of your current IT landscape.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}