Takeaways from the Google Cloud Public Sector Summit on Prioritizing Tech Investments
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Editor’s note: Today’s post highlights five takeaways from our session at the first ever Google Cloud Public Sector Summit. To watch the full session, check out All the Right Moves: Prioritizing Investments in Technology.
Now more than ever, government agencies need to invest in digital services to fulfill their missions and better serve communities. Yet modernization isn’t a one-and-done approach; it’s a sustained effort, with multi-year implications, and requires careful consideration of how to integrate existing investments to optimize costs. Digital transformation requires coordination between different programs and agencies, including all of their many competing considerations. In short, maximizing technology investments requires careful planning, strategic thinking, and industry partners that can provide flexibility and security and meet agencies where they are.
I sat down with Suzette Kent, former U.S. federal chief information officer, and Dominic Sale, assistant commissioner for Technology Transformation Services at the General Services Administration (GSA) for a conversation to unpack this important topic and discuss industry best practices.
The panelists had five tips for government employees who are making technology purchasing decisions for their agency.
1. Put the agency’s mission first.Avoid getting distracted by exciting new trends and focus on long-term goals that can impact which procurement strategies or funds could be used. The discussion started with how government agencies could cut through the noise about technology and prioritize which technology is best for their needs. Sale and Kent agreed that an agency should focus on its core mission outcomes and let its technology needs flow from that. Sale also emphasized the importance of having technology design respond to humans’ needs, which has historically been a challenge for government agencies.
For agencies to stay focused on their mission, final decisions about technology need to be made by the program manager who best understands each program’s mission. The government CIO’s role is to be the enabler for the technology and leverage it at the enterprise level, particularly when it comes to sharing infrastructure. The takeaway: enable mission programs by empowering your teams and providing access to authorized, compliant, innovative data platforms that programs can move confidently and quickly with.
2. Invest in interoperability. Agency employees often struggle to balance the need for a positive return on investment (ROI) with requirements for meeting mission objectives. While the panelists agreed that the total cost of ownership was important, they also emphasized taking an expanded view of ROI, including future-proofing and investing in functionality that may not realize its return for many years based on the initiative. Saving money isn’t particularly valuable if the solution doesn’t meet an agency’s needs. When choosing a technology partner, government employees should understand its long-term vision to ensure that the partner fits agency priorities. Partners’ technologies should also integrate seamlessly with existing systems so agencies don’t duplicate investment costs.
For example, Google Anthos extends Google Cloud services and engineering practices into an organization’s existing environment, establishing operational consistency across apps and modernization. With Anthos, agencies can simply and securely build and deploy applications anywhere, integrating cloud services across platforms. This allows them to enjoy a consistent DevOps experience for hybrid and multi-cloud environments and enables new innovation. Most importantly, this enables an enterprise data platform, one of the largest catalysts for mission transformation and applied AI.
3. Take advantage of artificial intelligence (AI) benefits. Over the course of the pandemic, the rapid application of AI has improved government productivity, efficiency, and the ability to deliver critical new services to the public at scale. This has further cemented AI’s role as an essential government technology for the present and future. In fact, Nextgov reports that “46% of government IT specialists plan to use AI and machine learning (ML) for embedded systems in the near future.”
As we’ve seen over the course of the pandemic, government programs can start small with AI pilots before moving into broad deployment. This can help agencies understand AI’s potential before moving to full production. People always supervise AI technologies, and the possibilities are endless. Google Cloud’s Contact Center AI (CCAI) has helped government agencies improve the customer experience, by allowing citizens to schedule vaccine appointments via a platform of their choice with up to 28 languages and dialects, and manage vaccine deployment. The U.S. Navy spends billions annually to fight rust and corrosion on its ships. Inspections of ships, aircraft and vehicles are a time-consuming and critical part of keeping the U.S. Navy at top performance so Google Cloud and Simple Technology Solutions (STS) rapidly built an AI-based corrosion-detection and analysis system. The system detected and analyzed corrosion on vessels with 90% accuracy and will eventually be used to automate inspections of vessels, aircraft, and vehicles—saving billions of dollars. Document AI helps a variety of government agencies scale their document processing, reducing the time it typically takes to process enormous amounts of data and related citizen claims.
Successful adoption of AI also depends on the quality of the data. Ultimately, agencies need high-quality enterprise data pipes so that employees and the community trust the system and public sector agencies. Sale described a GSA project that used AI bots to read legal contracts and look for particular phrases that would indicate a specific use case. Previously, an employee would have had to read through the contracts and search for the information. In this way, AI is saving the government both money and time.
4. Creating better experiences for the public. Sale observed that, “trust is the government’s currency and profit motive.” And trust comes when the public can be served with the same modern tools and technology they’re used to – in real-time and with transparency in mind. For example, agencies can provide transparency in public-facing dashboards for programs and supply services that deliver information in real-time through solutions like CCAI.
Trust also requires that constituents feel that government agencies will keep their data safe and secure. The need for a globally secure infrastructure with systems that are up-to-date and designed with security at every level, underpinned by zero-trust enterprise-wide remains paramount – particularly after the series of recent cyberattacks targeting government IT infrastructure.
5. Finding the right technology partner. Government leaders need technology partners who provide a flexible and interoperable platform to integrate existing investments and maximize technical value. Historically, public sector agencies have largely been forced to adopt private clouds, which has reduced their access to richer features, and hindered their ability to adopt a full range of security and product capabilities. The right partner won’t require government leaders to compromise on functionality or service availability to achieve compliance. The right partner can harness the power of emerging technology to make it Government-ready and the true promise of cloud– the access and integration of open data– to make missions more powerful and impactful for the constituencies they serve.
How Vertex Vizier’s Automated Hyperparameter Tuning Improves ML Models
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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:
- “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.
- 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.
- 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.
- 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.
- 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.
Google Cloud Celebrates Journey of 3 Inspiring Founders for the Asian Pacific American Heritage Month
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May is Asian Pacific American Heritage Month —a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights three AAPI founders from the Google For Startups community.
Read on to learn how these three founders built their businesses, leverage Google tech, and suggestion they have for aspiring entrepreneurs.
CultivatePeople
Founder: Lola Han
Description: CultivatePeople’s compensation software, Kamsa, provides global market pay rate data and helps companies make data-driven salary decisions so they can attract and retain their most valuable asset: employees.
Why GCP: “CultivatePeople began using GCP when integrating SSO/SAML authentication after our SaaS product became a high priority. We were able to exceed our clients’ expectations and increase their level of trust in our platform’s capabilities. We’re currently investigating additional machine learning products, such as Cloud AutoML, that will allow us to quickly deliver exciting features.”
Note from the Founder: “I grew up with immigrant parents who value stability and are risk-averse. My parents discouraged me from starting my own company because they didn’t want to see me struggle financially or see my health suffer (due to stress). I felt strongly about what CultivatePeople could do, so I started the company as a sole founder in 2017 and watched it double in size year over year since. While the ones I love most may not have cheered me on initially, it was important for me to hang on to the encouraging words of former bosses, executives, and founders to keep me focused on my mission.
My advice for other AAPI founders is to be a “silent assassin” and believe in the mission and values of your organization. Always remember to stop along the way and:
1) Enjoy the journey by celebrating wins and giving yourself credit;
2) Follow your intuition—it’s (almost) always right;
3) Recognize and invest in your people regularly (ie. give increases more than once a year, if warranted);
4) Give regular words of affirmation to employees on even small achievements.”
Swit
Founder: Josh Lee and Max Lim
Description: Swit is a team collaboration platform that seamlessly combines team chat with task management by allowing teams to turn their conversations into trackable tasks and share tasks to chat with simple drag-and-drop functionality, ensuring everyone is on the same page and projects get done faster.
Why GCP: “Swit is a cross-category hybrid work tool for chat and tasks. This functionality requires more complicated and heavier architecture for performance. So, configuring and managing virtual machines was really challenging to scale up our systems, while handling occasional unexpected traffic surges and frequent updates. Eventually we divided our monolithic architecture into 35 microservices when we launched our official product. The migration to GKE took around one month, and it turned out to be well worth the effort—our systems became able to offer high scalability and enough resilience to keep its uptime no matter what happened. Now we’re operating 84 workloads and 252 microservices with high stability with remarkably low downtime – less than 0.00001%/year.”
Note from the Founder: “As an AAPI founder based in Silicon Valley, I feel proud of the work ethics and diligence fellow Korean American entrepreneurs and professionals have long demonstrated here. Especially with K-pop breaking into the mainstream, I feel even more proud of our culture that strives toward an absolute perfection molded through years of training and dedication. The mission-driven culture of Silicon Valley coupled with Google’s edging technology and creativity really helped us build a product that not only encompasses verticals but also transcends cultures. Swit is growing at an unprecedented rate, and we hope to join the long list of successful AAPI entrepreneurs here. Swit’s close network with the AAPI community wouldn’t have been possible without Google support. We are grateful for this collaborative environment, and we hope to become the next-generation ambassador for collaboration after Google.”
Check out more from Swit in their founder story.
WISY
Founder: Min Chen
Description: Wisy develops technology to bring digital efficiency into the physical world, supporting consumer products businesses and making them thrive in the new economy. All of us have a bad experience when we can’t find the product we want to buy. That is a $1.9T problem in the consumer-packaged goods industry that Wisy is solving with AI and analytics to help manufacturers and retailers sell more by reducing out-of-stocks and waste at a global scale.
Why GCP: “GCP has an intuitive, easy to use interface, was lower cost, and offered preemptible instances with flexible compute options. Some of the reasons why Wisy decided to use GCP include instance and payment configurability, privacy and traffic security, cost-efficiency, and Machine Learning.
Wisy has been able to advance quickly with product development, as well as collaborate better and iterate faster in the creation of our AI models, while reducing costs by 40%. At Wisy, we are solving a problem that affects everyone who shops at a store.“
Note from the Founder: “Two years ago, I moved to San Francisco to expand my second startup, Wisy. This is when I learned that my name ‘Min’ stands for ‘minority.’ I was born in China, raised in a Black community in Panama, received scholarships to attend both Carnegie Mellon and UC Berkeley. I worked for 20 years in several countries, but I have never felt so discriminated against due to my race, ethnicity, gender and age than during my time in Silicon Valley. However, this is also the place I learned that my diverse life experience is my competitive advantage. My background enables me to recruit and relate to people in different countries, create scalable and flexible products for multinational customers, and run global operations efficiently.
My recommendation to AAPI founders is to find strength in their multicultural background. Don’t hide what makes you unique, do not limit yourselves, and do not let others limit you. You will lose your edge when trading authenticity for validation. Be proud and own your story.”
If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.To learn more about how you can help #StopAsianHate during Asian Pacific American Heritage Month and beyond, visit their website here.
Explore The New Era of Flexibility: Streamlined AWS-to-Google Cloud Migration
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As an IT leader, you’re asked to do it all: innovate and optimize your tech stack for business outcomes — all while being secure and compliant. It takes heroic efforts to achieve innovation and progress while also tightening budgets and teams. This is why many of you are considering migrating applications to Google Cloud, for benefits like scalability and flexibility, security and compliance, disaster recovery and business continuity, and cutting-edge technologies at lower costs.
To help you do this, we suggest using Google Cloud’s Migrate to Virtual Machines – part of Migration Center. This managed cloud service lets you lift and shift workloads at scale to Google Cloud Compute Engine with minimal changes and risk.
And recently, we rolled out our latest release which introduces support for workload migration from AWS to Google Compute Engine. With this addition, you can now migrate both your on-prem and AWS workloads at scale. This means centralized management for your end to end workload migration journey from both sources via Cloud Console or APIs.
Simple and easy migrations from AWS and VMware sources to Google Cloud
Migration of AWS EC2 instances directly to Google Compute Engine using Migrate to Virtual Machines follows a well established and easy journey, which means a minimal learning curve for users who are already migrating workloads from VMware. Workload migration is agent-less, which means you do not need to access or alter workloads as a prerequisite for migration, allowing you to execute zero-touch migrations. Migrate instance data with no interruptions to the running workload at the source for a fast cutover to Google Cloud. In addition, our end-to-end cloud console interface surfaces your AWS EC2 inventory, migrations, and groups so you can execute migrations without ever leaving the cloud console interface.
Large-scale migrations
Completing a large-scale migration project in a timely manner calls for careful planning and streamlined migration sprints. The Migrate to Virtual Machines’ Groups construct enables you to group source VMs together in the planning phase. When it’s time to execute the planned migration, VM Groups let you execute migration operations on a group level, or on a subset of the group, streamlining the process at scale.
Minimal downtime and risk
Application uptime is key to keeping your business running. Every migration with this latest release of the service periodically replicates data from the source workload to the destination without manual steps or interruptions to the running workload, minimizing workload downtime and enabling fast cutover to Google Cloud. You can also launch non-disruptive migration tests — referred to as test-clones — to help you validate that these workloads will work properly in the cloud before cutting over. This helps avoid issues that might have otherwise been costly or disruptive to your business.
How the service works
Migrations simply work, at scale, in a managed service fashion. With Migrate to Virtual Machines, there’s no requirement to provision or manage migration-specific resources in the cloud. The service uses replication-based migration technology to lift and shift workloads from source environments to Google Cloud. The Migrate Connection replicates source VM disk snapshots in the background with no interruption to the source workload. Replicated data is encrypted in transit and at rest, and when you instantiate a migrating VM using a test-clone or cut-over, the service seamlessly adapts your source VM operating system to boot and run natively in the cloud — including configuring network settings and deploying Google Cloud guest packages.
The migration journey of an EC2 instance — or VMware VM — to Google Cloud is comprised of the following steps:
1. Onboarding a source VM for migration: Onboard one or more VMs for migration from the source environment fleet.
2. Configure landing zone target: You can migrate an instance to any Google Cloud project in your environment and update landing zone details at any time before executing a test-clone or cutover.
3. Initiate VM data replication of source workload: Migrate to Virtual Machines periodically replicates instance disks to the cloud with no interruption to the source instance. You can control replication frequently and pause or resume at any point in time.
4. Test migrating instance: Test-clone creates a copy of your source instance in the defined landing zone to validate the migrating instance in the cloud before executing a cut-over. You can repeat the test-clone multiple times to multiple landing zones for thorough validation
5. Cutover migrating instance: Cutover operation shuts down your source instance and then performs the short final sync to Google Cloud. Migrated VM is instantiated in the target landing zone.
Getting started with Migrate to Virtual Machines
It’s quick and easy to start migrating your AWS EC2 instances and on-premises VMs today:
- Enable the vmmigration API in a Google Cloud project
- Create an AWS source in your environment
- Onboard and initiate replication of instance data from source
- Set migrating instance target details.
- Perform non-disruptive tests of your migrating instance using test-clone
- Cutover your instance to the cloud with minimal down time
You can also visit our website to learn more about Migrate to Virtual Machines. If you know you have to migrate in 2023 but aren’t sure how to get started, you can sign up for a free discovery and assessment of your current IT landscape so we can help craft the ideal migration plan for you and your business.
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IDC’s research demonstrates the value of running SAP environments on Google Cloud. Customers interviewed by IDC described achieving strong value through improved agility, high performance, and cost and staff efficiencies.
The business and operational benefits of running SAP environments on Google Cloud range from lowering downtime to improving productivity to enhanced efficiency.
Download this IDC infographic to understand the business value of migrating SAP environments top Google Cloud.
Groupe Dauphinoise Grows it Customer Base with G Suite and Google Cloud Platform
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As a leading French agricultural cooperative, Groupe Dauphinoise places collaboration at the heart of its philosophy. Working with farmers in the Rhone-Alpes region, Groupe Dauphinoise takes on a diverse range of activities from agricultural production to research and development to running retail outlets.
As its operations expanded and strained its existing infrastructure, Groupe Dauphinoise saw the opportunity to upgrade its technology solutions and adopted G Suite, Chrome devices and ultimately Google Cloud Platform (GCP).
“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream,” says Sylvain Claudel, Head of IT at Groupe Dauphinoise.
“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream.”
—Sylvain Claudel, Head of IT, Groupe Dauphinoise
Flexible workforce, stable infrastructure
Many of Groupe Dauphinoise’s 1,500 employees spend their days out of the office. Five years ago, with the help of Google partner GoWizYou, the company transitioned to G Suite to improve the flexibility and collaboration of its staff.
As Groupe Dauphinoise began to expand and collect more data, the company reached the limits of its on-premise infrastructure. Adding new storage was not a simple matter. Acquiring, configuring and synchronising new servers costs Groupe Dauphinoise time as well as money. In addition, with all its servers stored in a single room, security was a concern. Groupe Dauphinoise needed a new infrastructure.
Google Cloud Platform was the only solution in mind after Groupe Dauphinoise’s experience with G Suite and Chromebooks. Disruption was kept to a minimum thanks to Google’s licensing agreements with Microsoft products, allowing the company to migrate without affecting its operations.
After migrating its infrastructure to Compute Engine, Groupe Dauphinoise can let Google look after the security and maintenance. Cloud IAM makes it easy for Groupe Dauphinoise to hand out permissions to sensitive resources across a number of sites with maximum security and minimum fuss. The company placed its archives in Cloud Storage, while Cloud SQL allows it to continue to make use of its MySQL databases without disrupting the day to day business. Meanwhile, BigQuery provides Groupe Dauphinoise with the raw power to analyse large datasets quickly.
“Our company is growing and we have more and more data to collect and analyze like sales data, weather patterns or production numbers. With GCP, we have more than a single on-premise data store, so our disaster recovery plan is much more flexible. Compute Engine allows us to add more storage quickly and easily, without having to spend days synchronizing data and installing new servers. We have improved security and maintained the stability of our infrastructure while keeping costs down,” says Sylvain.
Safeguarding the present, looking to the future
With Google Cloud Platform, Groupe Dauphinoise has expanded and secured its infrastructure without sinking costs into on-premise servers or DevOps staff. Its investment in Chrome devices and adoption of G Suite mean that its mobile workforce can fully reap the benefits of a cloud-based infrastructure while dramatically cutting the cost of hardware. Meanwhile, working with a 200 million line table of sales data in Google BigQuery, the cooperative found that queries ran ten times faster than with its previous database provider. Groupe Dauphinoise is experimenting with Google BigQuery to expand its BI capabilities. Products like Google BigQuery help Groupe Dauphinoise grow its business, safe in the knowledge that its infrastructure is stable and secure.
“The amount of data we collect is growing very quickly. We need to break our rules and evolve from the mindset that we had with on-premise infrastructure and our old databases. We can now look at collecting more customer fidelity data, or big data for our farmers. With our data and infrastructure in Google’s care, we can concentrate on growing our customer base instead of our IT department!” says Sylvain.
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