We recently launched Vertex AI to help you move machine learning (ML) from experimentation into production faster and manage your models with confidence—speeding up your ability to improve outcomes at your organization.

But we know many of you are just getting started with ML and there’s a lot to learn! In tandem with building the Vertex AI platform, our teams are dropping as much best practices content as we can to help you come up to speed. Plus, we have a dedicated event on June 10th, Applied ML Summit, with sessions on how to apply ML technology in your projects, as well as grow your skills in this field. 

In the meantime, we couldn’t resist a quick lesson on hyperparameter tuning, because (a) it’s incredibly cool (b) you will impress your coworkers (c) Google Cloud has some unique battle tested tech in this area and (d) you will save time by getting better ML models into production faster. Vertex Vizier, on average, finds optimal parameters for complex functions in over 80% fewer trials than traditional methods. 

So it’s incredibly cool, but what is it?

While machine learning models automatically learn from data, they still require user-defined knobs which guide the learning process. These knobs, commonly known as hyperparameters, control, for example, the tradeoff between training accuracy and generalizability.  Examples of hyperparameters are the optimizer being used, its learning rate, regularization parameters, the number of hidden layers in a DNN, and their sizes.

Setting hyperparameters to their optimal values for a given dataset can make a huge difference in model quality. Typically, optimal hyperparameter values are found via grid searching a small number of combinations, or tedious manual experimentation. Hyperparameter tuning automates this work for you by searching for the best configuration of hyperparameters for optimal model performance. 

Vertex Vizier enables automated hyperparameter tuning in several ways:

1. “Traditional” hyperparameter tuning: by this we mean finding the optimal value of hyperparameters by measuring a single objective metric which is the output of an ML model.  For example, Vizier selects the number of hidden layers and their sizes, an optimizer and its learning rate, with the goal of maximizing model accuracy.
2. When hyperparameters are evaluated, models are trained and evaluated on splits of the data set. If evaluation metrics are streamed to Vizier (e.g. as a function of epoch) as the model is trained, Vizier’s early stopping algorithms can predict the final objective value, and recommend which unpromising trials should be early stopped. This conserves compute resources and speeds up convergence.
3. Oftentimes, models are tuned sequentially on different data sets. Vizier’s built in transfer learning learns priors from previous hyperparameter tuning studies, and leverages them to converge faster on subsequent hyperparameter tuning studies.
4. AutoML is a variant of #1, where Vertex Vizier performs both model selection, and also tunes architectures/non-architecture modifying hyperparameters. AutoML usually requires more code on top of Vertex Vizier (to ingest data etc), but Vizier is in most cases the “engine” behind the process. AutoML is implemented by defining a tree like (DAG) search space, rather than a “flat” search space (like in #1). Note that you can use DAG search spaces for any other purpose where searching over a hierarchical space makes sense.
5. There are times when you may wish to optimize more than one metric. For example, we would like to optimize model accuracy, while minimizing model latency. Vizier can find the Pareto frontier, which presents tradeoffs for multiple metrics, allowing users to choose the appropriate tradeoff. Simple example: I want to make a more accurate model, but would like to minimize serving latency. I do not know ahead of time what’s the tradeoff between the two metrics. Vizier can be used to explore and plot a tradeoff curve, so users can select on the most appropriate one. For example, “a latency decrease of 200ms will only decrease accuracy by 0.5%”

Google Vizier is all yours with Vertex AI

Google published the Vizier research paper in 2017, sharing our work and use cases for black-box optimization—i.e. The process of finding the best settings for a bunch of parameters or knobs when you can’t peer inside a system to see how well the knobs are working. The paper discusses our requirements, infrastructure design, underlying algorithms, and advanced features such as transfer learning that the service provides. Vizier has been essential to our progress with machine learning at Google, which is why we are so excited to make it available to you on Vertex AI.

Vizier has already tuned millions of ML models at Google, and its algorithms are continuously improved for faster convergence and handling of real-life edge cases. Vertex Vizier’s models are very well calibrated and are self-tuning (they adapt to user data), and offer unique power features, such as hierarchical search spaces and multi-objective optimization. We believe Vertex Vizier’s set of features is a unique capability to Google Cloud, and look forward to optimizing the quality of your models by automatically tuning hyperparameters for you.

To learn more about Vertex Vizier, check out these docs and if you are interested in what’s coming in machine learning over the next five years, tune in to our Applied ML Summit on June 10th, or watch the sessions on demand in your own time.

Quanta Services is the leading specialty contractor with the largest and highly-skilled trained workforce in North America. It provides fully-integrated solutions for the electric power

pipeline industrial and telecommunications industries the company’s geographic footprint which includes North America Latin America and Australia.

It’s network of companies ensures world-class execution with local delivery and has over 40,000 employees.

“The exciting thing about Backstory is it allows us to land massive amounts of data from all of our different security tools and then Chronicle worries about how to correlate and aggregate this information for us. We can then focus on the highest priority threats.”

Richard Breaux, Manager Security Operations Quanta Services

Because Quanta Services’ customers provide energy and telecommunications to their customers, they have world-class cybersecurity requirements.

“We must ensure that Quanta leads the industry given the fact that our business is building the core infrastructure that powers people’s lives. It’s crucial that we meet their cybersecurity requirements,” says James Stinson, VP-IT, Quanta Services.

To support this goal Quanta implemented a number of security tools that generate terabytes of security telemetry. Over time, however, these systems generated an information overload for the company’s limited pool of qualified security resources.

“Our logging tools also couldn’t keep up with the rapidly growing information. With limited resources we need to focus our security analysts time on high quality work instead of digging through mountains of data,” says Stinson.

They achieve this by implementing Backstory and Chronicle, which is on Google Cloud.

“The exciting thing about Backstory is it allows us to land massive amounts of data from all of our different security tools and then Chronicle worries about how to correlate and aggregate this information for us. We can then focus on the highest priority threats,” says Richard Breaux, Manager Security Operations Quanta Services.

As a result, the company’s security  team spends less time getting to the core information they need to address these incidents.

“What used to take us 15 minutes or more, we can now accomplish in seconds,” says Breaux.

Over the past decade, cybersecurity has posed an increasing risk for organizations. In fact, cyber incidents topped the recent Allianz Risk Barometer for only the second time in the survey’s history. The challenges in combating these risks only continue to grow. Adversaries tend to be agile and are consistently looking for new ways to land within your digital environments. They also drive attack vectors that work, which means enterprise risk leaders are now forced to look for new ways of securing infrastructure and data.

Cloud comes of age in the modern day cybersecurity threat landscape

When cloud, in its various delivery models, was first introduced, it didn’t fit neatly into the security frameworks that had seemingly protected networks for many decades. Public cloud was the answer to ongoing IT challenges: scale, resources, security capabilities, and budget cycle limitations. Now, public cloud is meeting the increasing challenge of implementing cybersecurity controls and frameworks that are capable of protecting today’s global enterprise.

Cloud adoption – with all its scale and redistribution of longstanding security paradigms – is the optimal choice for infrastructure and security, particularly as organizations grapple with the need to engage in digital transformation. We assert that successful digital transformation is impossible without incorporating the use of the scale, security architecture, and resiliency of the cloud.

Consequently, cloud adoption becomes a necessary component of roadmap discussions and planning as your organization looks to reduce overall risk. Risk leaders and enterprise cybersecurity leaders must consider that moving data, digital processes, and priority workloads to the public cloud is a crucial step for meeting the current and future digital needs of the enterprise. Going forward, this digital transformation increasingly will include hybrid infrastructure environments composed of a combination of on-premises and cloud solutions.

Pinpointing where threats thrive

As digital environments become more complex within a given organization, proactively countering adversaries becomes all the more difficult. It’s harder to implement, scale, and adhere to existing security and control frameworks. It’s also increasingly challenging to apply framework guidance to new applications, build and support infrastructure within a secure foundation, and maintain good cyber hygiene through the digital lifecycle.

As reported by TechTarget, the 2020 hack of the SolarWinds Orion IT performance monitoring system is a prime example. It grabbed headlines “not because a single company was breached, but because it triggered a much larger software supply chain incident.” This vulnerability in popular, commercially available, and widely utilized software compromised the data, networks, and systems of thousands of companies when a routine software update turned out to be backdoor malware.

A close look at the root problems behind high-profile security breaches reveals that it’s a lack of agility and an inability to scale resources that prohibit the modern security organization’s ability to respond quickly enough to counter new challenges. Look even closer and you’ll often find an insufficient implementation of best practices and ineffective solutions, leaving an organization continually chasing the next tool or solution and scrambling to stay ahead of emerging threats.

While the cost to individual businesses is high, most organizations struggle with the needed skills and resources to rigorously maintain data security basics and ensure readiness for inevitable attacks. The previous sentence is especially true when you consider that maintaining an effective state of cybersecurity readiness is a costly practice that requires the continual development of expertise, the evaluation of new tools, and an ongoing element of vigilance.

Threat visibility is a big part of the problem. You can’t protect your company from what you can’t see. For individual enterprises – with critical data workloads housed in a combination of on-premises servers, a variety of endpoints, and both private and public cloud instances – staying ahead in the ongoing battle requires a new approach.

The identification of actionable alerts and other data contributes to a better overall state of readiness. Thought leadership and discussions related to Autonomic Security Operations provide a promising outlook for security organizations willing to lean into the changing technology landscape – a landscape that now benefits from leveraging automation and machine learning currently used in security stacks. Reducing the chance of introducing vulnerabilities or missing-critical alerts starts with ensuring full visibility into an increasingly expanding and complex environment.

The evolution of a shared responsibility to a shared fate

Industry megatrends are driving cloud adoption and with it a path to improved cybersecurity. Among these trends is the concept of shared fate as an evolution of the historical shared-responsibility model. Shared fate drives a flywheel of increasing trust which develops as more enterprises transition to the cloud. This compels an even higher security investment and a more vested interest from cloud service providers.

At Google Cloud, shared fate means we take an active stake in our customers’ security posture, offering capabilities and defaults that help ensure secure deployments and configurations in the public cloud. We also offer experience-based guidance on how to configure cloud workloads for security, and can assist with risk management, reduction, and transfer.

The Google Cloud Risk Protection Program represents the continuing evolution of the shared-fate model. The program offers a practical solution that provides the modern enterprise a snapshot comparison of its current security state against well-adopted cloud-security frameworks. It also give you an opportunity to explore cyber insurance designed to meet your needs from our partners Allianz and Munich Re.

When performed with diligence, cloud adoption can help increase your overall cybersecurity effectiveness. Using a hybrid approach – and steadily reducing which data assets remain on premises – can strengthen your overall security posture and reduce risks to the organization.

Cloud security and the ability to reduce risk

In comparison to the enterprise-by-enterprise security scramble to protect data and workloads in individual private clouds, global public cloud solutions like Google Cloud can be a force multiplier when adhering to established best practices. By that, we mean, quite literally, that you get more security at every touchpoint – from infrastructure and software to access and data security.

Strong security in the public cloud starts with the foundational pieces: the hardware and design elements. At Google, for example, we take a security-by-design approach within both the data center and purpose-built components themselves. Within Google Cloud, data is encrypted by default – both at rest and in transit. Google’s baseline security architecture adheres to the zero trust principles, meaning that every network, device, person, or service initially cannot be trusted.

Embarking on a zero trust architecture journey gives modern security practitioners the ability to methodically shut down traditional attack vectors. Zero trust also provides more granular visibility and control of rapidly expanding environments. The recent emphasis of its benefits, as the U.S. White House set forth through an executive order on increasing cybersecurity resilience, is an example of the wide-scale recognition by both government and industry on the benefits of this approach.

Since adopting a zero trust approach more than a decade ago, Google has achieved a recognizable level of maturity, reflected by our internal infrastructure and multiple enterprise offerings, enabling different aspects of the zero trust security journey.

Compliance and privacy drive critical elements of the cloud adoption cycle

Privacy frameworks, regulatory compliance, and data sovereignty are driving critical elements of the cloud adoption cycle. Cloud providers must ensure they have the necessary controls, attestations, and abilities to audit in order to provide organizations with the tools to preemptively satisfy regulatory and compliance mandates across the globe.

Now consistently expected to be part of a design feature that’s built into the cloud journey, it cannot simply be an add-on capability. The direction of this evolution promises to play more of a role in the future of cloud adoption, not less. Because this is an ongoing component of enterprise risk evaluation, your business must consider cloud providers that can partner on this critical aspect of the journey – and not leave you without the resources to respond to this growing critical need.

Building trust into your digital transformation journey

Digital transformation is difficult because the modern enterprise must build and design for both today and tomorrow. From a security perspective, the challenge has often been that security industry practitioners cannot always predict what the future will look like. That said, there are clear steps you can take to mitigate all-around risk throughout the process.

How you approach the cloud is, of course, integral to your journey, but it doesn’t need to be an all-or-nothing proposition. And although technology debt continues to persist with legacy systems, that doesn’t mean you shouldn’t begin to move forward.

Google Cloud enables you to modernize at your own pace and understand what’s realistic. We recommend you move what data you can to a more secure public cloud today, followed by a phased approach to move more in the months and years that follow. The key tenets of our approach to security in the public cloud include:

• The security-by-design posture of Google Cloud can help modern-day enterprises scale security capabilities and reduce risk with an architecture built on zero trust principles.
• The Google Cloud approach to security and resiliency includes a framework to help you protect against adverse cyber events by using our comprehensive suite of solutions.
• Google Cloud can help ensure your organization adheres to the requirements of a growing and increasingly complex regulatory and compliance environment.
• The ideal model of a future organization is one where cloud plays a major role in infrastructure design and architecture. Your organization should begin to view public cloud as an enabler of the business and a core component of digital transformation.

As you transition more data to the public cloud, it’s paramount that trust is ingrained in every step you take with your cloud service provider. Many service providers readily take on a shared responsibility with your organization when it comes to security. At Google, we take it several steps further with our shared fate model to help ensure data security in the public cloud. Your future and our’s are part of the same data security journey.

The financial services industry is evolving at a rapid pace, with shifting consumer expectations, new technologies, and developing regulatory requirements. Financial services firms need the right technology to help them stay agile and prepare for the future. 

The cloud is a key point of leverage for firms looking to improve performance across a broad range of activities. Moving to the public cloud can advance operational resiliency, improve staff productivity, increase regulatory compliance and enhance business model innovation. 

However, there are a number of financial services companies that are still hesitant in their cloud journeys. The barriers to adoption vary, from the complexity of legacy systems, to trust and skills gaps, regulatory uncertainty, and fragmentation of compliance requirements. Although many companies have embraced the benefits of cloud technology, more robust cloud adoption—especially around core back-office functions—will require additional facilitation, including through regulatory harmonization and streamlining.

A new comprehensive study on cloud adoption in financial services

To better understand the challenges and opportunities of cloud adoption in financial services Google Cloud, together with the Harris Poll, surveyed more than 1,300 leaders from the financial services industry across the United States, Canada, France, Germany, United Kingdom, Hong Kong, Japan, Singapore and Australia. 

There were five noteworthy takeaways from the study:  

1. A vast majority of financial services companies are already using some form of public cloud. A large number of surveyed financial services companies (83%) report they are deploying cloud technology as part of their primary computing infrastructures. Of those using cloud technology, the most popular architecture of choice is hybrid cloud (38%), followed by single cloud (28%), and multicloud (17%). Notably, of respondents without a multicloud deployment, 88% reported they are considering adopting a multicloud strategy in the next 12 months.

2. Financial services institutions in North America are leading in cloud adoption. Of the financial services companies who are implementing a cloud strategy, the highest levels of cloud workload adoption were reported in North America, with institutions in the U.S. (54%) and Canada (52%) leading the way. The lowest level of cloud adoption was reported in Japan (42%).

3. As financial services companies continue to use the cloud, more core functionalities can and will be migrated. While many financial services companies have migrated substantial workloads to the cloud, the industry is far from full adoption when it comes to core, back-office workloads. Of financial services companies currently using a majority cloud strategy in the United States, for example, only half (54%) of their workloads are fully deployed in the cloud. Data and IT security (74%), regulatory reporting (57%), and fraud detection and prevention (57%) rank among the highest workload adoption. Core underwriting activity (40%) and data reconciliation (48%) ranked lowest. Across Europe, cloud usage for core activities like underwriting also scored low with the UK listing only 30% adoption.

4. Among respondents, there is a very strong positive perception of the potential for cloud technology to assist in business operations and regulatory compliance. Nearly all respondents (>88%) agreed that cloud adoption can:

1. help adapt to changing customer behaviors and expectations,  
2. enhance operational resilience, 
3. support the creation of innovative new products and services, 
4. enhance financial services institutions’ data security capabilities, and 
5. better connect siloed legacy software infrastructure within financial services institutions. 

5. Certain regulator-induced challenges, including the complexity of sectorial compliance frameworks and fragmentation, create hurdles to cloud adoption for financial services companies. While 88% of respondents had a positive view of current regulatory efforts to provide guidance and clarity for cloud implementation, the results showed that more needs to be done to facilitate adoption. Most respondents (84%) agree that regulatory reviews and approvals take too long because of regulatory fragmentation across regulatory bodies. And 78% say that regulatory uncertainty over the use of public cloud prevents their organizations from adopting cloud technologies that would otherwise provide benefit to them. Additionally, a third of all on-premises respondents (38%) say that the large investment of resources for the regulatory approval process is a reason why they’re not using cloud services.

“While many banks have already deployed hybrid cloud environments, others are still in various stages of planning and deploying,” said Jerry Silva, research vice president for IDC Financial Insights. “Clearly, hybrid infrastructure is a reality, and financial institutions must focus not only on leveraging the modern infrastructure model to gain efficiencies, resilience and agility, but also on taking the necessary steps to manage such environments, including the security and compliance of cloud services.”

Future recommendations for financial services regulators

Financial services firms should continue to maximize the potential of technology by migrating more core workloads to the cloud, and actively considering multicloud and hybrid-cloud strategies. Such strategies enhance resiliency of existing IT infrastructure and reduce concerns over vendor lock-in. 

The research also points to steps that regulators could take to provide additional clarity and guidance, such as aligning regulatory reviews across agencies to avoid fragmentation; developing regulatory “safe harbors” for cloud adopters based on adherence to accepted standards and best practices; training regulatory staff on emerging tech; and advancing data reporting requirements via cloud and related technologies.

In the past few years, many regulators across the globe have taken a robust approach to rationalizing rules and guidance to cloud adoption in the financial sector, which has helped significantly stimulate adoption. But further assurances and harmonization of best practices around supervision is needed to advance risk-based and secure digital innovation.  

At Google Cloud, we’re committed to working with financial services customers and regulators to provide them with controls and assurances on risk management, data locality, transparency, and compliance. We are constantly engaging with regulators to share information, respond to their considerations and concerns, and address questions in the interest of transparency and building trust. 

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

^{Research methodology}

^{The survey was conducted online by the Harris Poll on behalf of Google Cloud, from December 7, 2020, to January 4, 2021, among 1,363 senior executives in France (n=113), Germany (n=178), the UK (n=192), Hong Kong (n=99), Indonesia (n=100), Japan (n=142), Singapore (n=71), Australia (n=134), Canada (134), and the United States (n=200) who are employed full-time, part-time, or self-employed whose main functional role is in risk/compliance or IT at a company in the banking, finance, or financial services industry with a title of director level or higher. The data in each country were weighted by the 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.}

The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.

However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.

The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.

Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.

In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.

Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.

BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.

THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.

While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.

As for performance, look at this chart:

With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.

Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.

With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.