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How a robotics startup switched clouds and reduced its Kubernetes ops costs with GKE Autopilot

Don’t look now, but Brain Corp operates over 20,000 of its robots in factories, supermarkets, schools and warehouses, taking on time-consuming assignments like cleaning floors, taking inventory, restocking shelves, etc. And BrainOS®, the AI software platform that powers these autonomous mobile robots, doesn’t just run in the robots themselves — it runs in the cloud. Specifically, Google Cloud. But that wasn’t always the case. Brain Corp recently partnered with Google Cloud to migrate its robotics platform from Amazon EKS to Google Kubernetes Engine (GKE) Autopilot. Running thousands of robots in production comes with tons of operational challenges, and Brain Corp. needed a way to reduce the day-to-day ops and security maintenance overhead that building a platform on Kubernetes (k8s) usually entails. Just by turning on Autopilot, they’ve offloaded all the work of keeping clusters highly available and patched with the latest security updates to Google Cloud Site Reliability Engineers (SREs) — a huge chunk of work. Brain Corp’s ops team can now focus on migrating additional robots to the new platform, not just “keeping the lights on” with their k8s clusters.Making the switch to Google CloudWhen Brain Corp decided to migrate off of EKS, they set out to find a cloud that had the best technology, tools, and platform to easily integrate data and robotics. Brain Corp’s Cloud Development team began by trying to implement a proof-of-concept architecture to support their robots on Google Cloud and another cloud provider. It became clear that Google Cloud was the right choice when it only took a week to get the POC up and running, whereas on the other cloud provider it took a month. During the POC, Brain Corp realized benefits beyond ease of use. Google’s focus on simple integration between its data products contributed significantly to moving from POC to production. Brain Corp was able to offload Kubernetes operational tasks to Google SREs using GKE Autopilot, which allowed them to focus on migrating robots to their new platform on Google Cloud. Making the switch to GKE AutopilotAlex Gartner, Cloud Infrastructure Lead at Brain Corp, says his team is responsible for “empowering developers to develop and deploy stuff quickly without having to think too hard about it.” On EKS, Brain Corp had dedicated infrastructure engineers who did nothing but manage k8s. Gartner was expecting to have his engineers do the same on standard GKE, but once he got a whiff of Autopilot, he quickly changed course. Because GKE Autopilot clusters are secure out of the box and supported by Google SREs, Brain Corp was able to reduce their operations cost and provide a better and more secure experience for their customers. Another reason for switching to Autopilot was that it provided more guardrails for developer environments. In the past, Brain Corp development environments might experience cluster outages because of a small misconfiguration. “With Autopilot, we don’t need to read every line of the docs on how to provision a k8s cluster with high availability and function in a degraded situation,” Gartner said. He noted that without Autopilot they would have to spend a whole month evaluating GKE failure scenarios to achieve the stability Autopilot provides by default. “Google SREs know their service better than we do so they’re able to think of failure scenarios we’ve never considered,” he said, much less replicate. For example, Brain Corp engineers have no real way to simulate out-of-quota or out-of-capacity scenarios.How has GKE Autopilot helped Brain Corp?Since adopting Autopilot, the Cloud Infrastructure team at Brain Corp has received fewer pages in the middle of the night because of a cluster or service going down unexpectedly. The clusters are scaled and maintained by Google Cloud. By imposing high-level guardrails on the cluster that you can’t disable, Autopilot “provides a better blast shield by default,” Gartner said. It also makes collecting performance metrics and visualizing them in Grafana dashboards drastically easier, since it exports tons of k8s and performance metrics by default. “Now we don’t need to spend time gathering or thinking about how to collect that information,” he said.Autopilot has also improved the developer experience for Brain Corp’s software engineers. They run a lot of background computing jobs and traditionally have not been able to easily fine-tune pod-level cost and compute requirements. Autopilot’s per-pod billing increases transparency, allowing devs to know exactly how much their jobs cost. They’ve also been able to easily orient compute requirements to the pods themselves. Billing at the app level instead of the cluster level makes chargeback easier than overprovisioning a cluster that five teams use and figuring out how to split the bill. “We don’t want to spend time optimizing k8s billing,” Gartner said. Cutting costs has been a huge advantage of switching to Autopilot. According to Gartner, there’s a “5-10% overhead you get billed for by just running a k8s node that we are not billed for anymore. We’re only paying for what our app actually uses.”How can Autopilot improve? GKE Autopilot launched last year, and isn’t at full feature parity with GKE yet. For example, certain scientific workloads require or perform better using specific CPU instruction sets. “GPU support is something we would love to see,” Gartner said. Even so, the benefits of GKE Autopilot over EKS far outweighed the limitations, and in the interim, they can spin up GKE Standard clusters for specialized workloads.With all the extra cycles that GKE Autopilot gives back to Brain Corp’s developers and engineers, they have lots of time to dream up new things that robots can do for us — watch this space. Curious about GKE and GKE Autopilot? Check out Google Cloud’s KubeCon talks available on-demand.Related ArticleGoogle Cloud at KubeCon EU: New projects, updated services, and how to connectEngage with experts and learn more about Google Kubernetes Engine at KubeCon EU.Read Article
Quelle: Google Cloud Platform

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Google Cloud simplifies customer verification and benefits processing with Document AI for Identity cards

If you’ve opened an account at a bank, applied for a government benefit, or provided a proof of age document on an ecommerce website, chances are you’ve had to share a physical or digital copy of a Driver’s License or a passport as proof of your identity. For businesses or public sector organizations that need this information to provide services, processing images of identity documents has long been a time- and resource-intensive process that requires extensive human intervention. Solutions exist to help digitally capture the data, but they require extensive human intervention that impacts the speed and cost of processing and ultimately the time to service customers.The Google Cloud Document AI family of solutions has been designed to help solve some of the hardest problems for data capture at scale by extracting structured data from unstructured documents to help reduce processing costs and improve business speed and efficiency. Today, we’re announcing the general availability of identity parsers that bring the power of Document AI to customer verification, KYC, and other identity-based workflows. With Document AI for Identity, businesses can leverage automation to extract information from identity documents with a high degree of accuracy, without having to bear the cost and turnaround time of manual tasks by a service provider. Document AI for Identity leverages artificial Intelligence to provide a set of pre-trained models that can parse identity and supports US driver’s licenses (generally available), US passports (generally available), French driver’s licenses (preview) and French National ID cards (preview), with more documents to be added from around the world over the coming months.When our customers process high-volume workloads or complex workflows, they need a high degree of accuracy, since getting the first step wrong can derail the entire workflow. The introduction of special parsers for Identity processing can help solve one of the most commonly required document processing needs that our financial services and public sector customers face.Along with the identity parsers, Google Cloud is also offering its“Human in the Loop” service, in which verification for a subset of identity documents can be automatically assigned to a pool of humans (internal or external) for manual review, based on confidence scores. While there are multiple industries and applications that could benefit from Document AI for Identity, we’ve seen two main kinds of applications being adopted during the solution’s preview. One is around processing ID cards uploaded as unstructured images at scale, so that enterprises can have IDs on file. The second use case is to perform advanced checks on identity documents to validate their authenticity and / or to detect fraud. Google Cloud’s fraud detector API (which is currently in preview) can complement Document AI for Identity and apply an extra layer of normalization to help validate the identity as a government-issued ID by checking for suspicious words, image manipulation, and other common issues with forged identity documents. With new versions of driver’s licenses being frequently released, Document AI for identity uses specialized models and constantly-updated training data to help make sure the parsers can offer a high degree of accuracy. For all use cases, Document AI does not retain any customer data after completing the processing request (successfully or with an error). Check out this demoand visit the Document AI for Identity landing page for more information on how Document AI can help solve your identity processing needs, and ask your Google Cloud account team to help you integrate Identity Document AI into your workflows.Related ArticleSnap Inc. adopts Google Cloud TPU for deep learning recommendation modelsSnap, Inc. is using Google Cloud solutions to quickly turn millions of data points into personalized customer ad recommendations.Read Article
Quelle: Google Cloud Platform

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“Take that leap of faith” Meet the Googler helping customers create financial inclusion

What brought you to Google?Honestly, the opportunity fell in my lap. I had just graduated from NYU with an MS in business ops and hospitality, and a staffing agency for Google reached out—they were looking for hospitality majors to support recruitment, hosting an engineer for the day as they walked through their interview process. I took the chance!Can you tell us a little bit more about your role as senior program manager at Google Cloud?I am the lead program manager across our program to grow Cloud with Black+ -owned businesses. We created this program to help enable digital acceleration for institutions playing a crucial role in combating systemic racism, and to increase their presence in the financial services industry. In my role, I work directly with customers, bringing together their vision with our engineering and innovation, to help them see their future on cloud.I’m proud to share that by aligning our mission with the right partners, the team has identified, integrated, and onboarded seven Black-owned financial services institutions onto Google Cloud. For example, we worked with First Independence, a black owned bank headquartered in Michigan who has been serving the local community—including small businesses—for 52 years. We partnered with a digital lending platform to help them digitize their loan process, allowing clients to quickly and easily apply for loans under the federal Paycheck Protection Program (PPP loans). Without the new tech infrastructure, many of their clients may have missed the opportunity to get this federal – and for many businesses, critical – support due to slow processes.We started small and learned a lot along the way, now we want to expand to other industries. Helping one bank at a time creates a lasting impact. (You can read more about the banks here)How do you feel like your background in hospitality supports your current role?I like to think of myself as a problem solver. It’s very cliche, but I love really working with people and helping them figure out how to get to their end goal. In this particular role, it’s working with customers, specifically financial institutions, that didn’t trust putting financial information on the cloud. Once I started to engage with these customers, I was able to build trust with them through a larger goal of helping the community. Once we built that rapport, they felt more comfortable. I want to help our underbanked communities be financially secure, have financial literacy and build generational wealth.We now have other industries that have heard about us and want to learn more about the program.Why do you think that cloud is well positioned to help advance financial inclusion?We all know about the wealth gap. We all know about the education gap. Cloud technology can help, Cloud’s scale and flexibility could actually change the lives generationally of people that need help.We can really shift the focus on not just saying Black Lives Matter, saying a name, or wearing a t-shirt, but also empowering organizations to grow their impact and better serve their communities.How would you describe your experience at Google?To begin my career here as a TVC and wanting to be a full-timer; to getting a role right before a pandemic, then being promoted last year, I still can’t believe it. I couldn’t be at a company that I didn’t align with what they were putting out there. I’m not a fake it ‘til you make it kind of person. I’m very honest and transparent. And, I feel that Google, at its core, is a great company. Do you have advice for other people who may want to align their passion with their profession?Take that leap of faith. I followed a great manager to this role, took a chance, and am so glad I did.Related ArticleMeet the people of Google Cloud: Jim Hogan, driving innovation through inclusionJim Hogan shares his experience as an autistic Googler and how inclusion drives innovation.Read Article
Quelle: Google Cloud Platform

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Google Cloud enables inclusive financial services with Black-owned business initiative

In June of 2020, Sundar Pichai outlined a number of commitments Google will make to racial equity, starting with the Black+ community in the United States. As part of this initiative, we formed a team at Google Cloud to help black entrepreneurs accelerate the growth of their businesses with cloud technology. Racial equity is inextricably linked to economic opportunity. According to McKinsey, advancing racial equity would create new oppor­tunities for participation in the economy for underrepresented individuals, resulting in significant benefits to businesses, families, and communities across the country. Black-owned financial institutions play a vital role in closing the racial wealth gap by providing greater access to financial products and services to historically underrepresented and underserved communities. That is why we decided to focus our initial efforts on empowering Black entrepreneurs and Black businesses in the financial services industry.   Together with partners like Uncommon Impact Studio, World Wide Technology, and Zencore, we aim to bring data, technology, and marketing capabilities that are uniquely Google to Black-owned banks and fintechs. By implementing cloud technologies, seven Black-owned financial institutions have been able to accelerate their digital transformation, scale their business, and connect their products and services to people that need them most. Let’s dive a little deeper into a few companies that are part of the initiative:  BetaBank: Improving access to capital for small businesses BetaBank recently announced its FDIC application to become one of the first digitally native banks built from the ground up on Google Cloud. BetaBank founder Seke Ballard recognized early that the financial lending system was broken, and he identified technology as the key to removing bias from small business lending. Ballard created an AI algorithm to weigh risk and calculate qualification of an SMB loan application with more accuracy, speed, and at a lower cost than traditional banks.BetaBank’s mission is to provide small business owners equitable access to financial services. Ballard and his team selected Google Cloud as the cloud infrastructure on which to build, run, and manage BetaBank. Google Cloud will provide a scalable, secure infrastructure  to grow BetaBank’s business and networks, and the tools to support regulatory compliance, fraud prevention, and overall security. OneUnited Bank: Delivering personalized customer experiences OneUnited Bank is one of the first and largest Black-owned digital banks in the United States.   OneUnited Bank worked with Google Cloud to implement Contact Center AI, a Google Cloud platform that enables companies to leverage AI to transform the performance of its call centers. The company also implemented Google Ads Search campaigns to connect with new customers.We recognize that there are things we can do that will 10x this company and there are ways that Google can help us Jim Slocum CIO One UnitedOneUnited paired Contact Center AI with its existing technology, and leveraged DialogFlow, Google Cloud’s conversational AI natural language understanding platform, to create a more personalized customer experience and scale their contact center interactions.  The success of the deployment was revelatory to OneUnited as to what cloud and AI technologies can do for them and their customers. First Independence Bank: Modern infrastructure for better community lending First Independence Bank is the only Black-owned bank headquartered in Michigan and has been serving the local community in Detroit for over 52 years. To ensure the bank could compete in the future, its legacy systems needed a digital upgrade. In September 2021, First Independence Bank partnered with a digital lending platform for business banking, to speed up its digital federal Paycheck Protection Program (PPP) loan application process  as a convenience to its PPP loan applicants. As part of this partnership, First Independence Bank has committed to migrate onto Google Cloud to create a more efficient lending process for customers.Data Capital Management: Harnessing the power of AIData Capital Management(DCM) is a digital investment research, services and advisory firm whose CEO and Co-Founder Michael Beal knew early on the power that artificial intelligence (AI) and machine learning (ML) can bring to the fund management industry. DCM worked closely with Google Cloud engineers to enhance its current offerings of DCM AI models (“AI Traders”) that investors can leverage to manage their stock holdings and digital wallets. Training AI models requires massive amounts of data and compute power. As the firm’s operations grew, the opportunity to optimize performance with Google Cloud was a primary factor for the decision to migrate DCM’s DCM.ai investor portal and all supporting investment research, execution, and reporting features from their legacy provider to Google Cloud.  What’s next? Building on our commitment to increase racial equity through technology, we are expanding this program beyond financial services to bring the full value of Google Cloud to other industries including education, entertainment, healthcare, and clean energy. If your company is interested in getting involved, please fill out this form.Related ArticleMeet the people of Google Cloud: Priyanka Vergadia, bringing Google Cloud to life in illustrationsWhen COVID shut down our world, Developer Advocate Priyanka Vergadia found ways to connect with the developer community through illustrat…Read Article
Quelle: Google Cloud Platform

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Azure NC A100 v4 VMs for AI now generally available

AI is revolutionizing the world we live in—from the way we entertain ourselves, to the products and services that we consume, to the way we care for our bodies, and how we go about our daily work. Organizations are leveraging the power of AI to transform our lives by accelerating superior product innovations, increasing organization competitiveness no matter their size or available resources, and immersing us into more amazing, photo-realistic virtual worlds in movies and games.

At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. With the power and scalability available through Microsoft Azure, we provide the compute tools and capabilities for all organizations no matter their size or resources to do more, faster. AI is a key tool to help organizations innovate and create new capabilities, discover new insights and deliver superior products and services. 

At Microsoft, our NC series virtual machines (VMs) allow our customers and partners access to almost limitless AI hardware infrastructure with Microsoft tools and services so that they can be productive quickly and more easily.

Here are some examples of what our customers are doing with the power of Azure AI:

“As scale complexity of advanced node integrated circuits increases, we are committed to provide our customers with innovative simulation technologies to reduce design and signoff cycles. Synopsys PrimeSim Continuum Solution delivers 10 times faster simulation with signoff accuracy using Azure’s NC A100 v4 virtual machines. The flexible form factor of NC A100 v4 VMs allow our customers to choose one, two, or four GPUs to fit the circuit size with the expected speedup of running on GPU.”—Hany Elhak, Sr. Director, Circuit Simulation Solutions, Synopsys

“In the era of large language models that often require tens to hundreds of Petaflops of GPU resources, how can a research lab in academia stay competitive? I think the answer is in cloud computing. Recently our strategy has been rigorously testing each new idea in a small scale until we have clear evidence that it works. And once we are sufficiently confident with it, we can radically scale it up with Azure’s high-end GPU virtual machines such as NC A100 v4 equipped with NVIDIA A100 80GB Tensor Core GPUs. This strategy has allowed us to become an active player in research areas that were formerly deemed to be only for industry labs.”—Minjoon Seo, Assistant Professor and Director of Language and Knowledge Lab, KAIST

“For our MCity initiative, Touchcast leverages cloud-based rendering on Microsoft Azure GPU-optimized virtual machines. In our "Metaverse City", Touchcast delivers photorealistic 3D environments that bring organizations, employees, and customers together in a brand-new digital world. Best of all, MCity transports any Microsoft Teams user into stunning and immersive metaverse venues for meetings, presentations, workshop sessions, shopping experiences, and more."—Edo Segal, CEO and Founder, Touchcast

Introducing the new NC A100 v4 series virtual machine, now generally available

We are excited to announce that Azure NC A100 v4 series virtual machines are now generally available. These VMs, powered by NVIDIA A100 80GB PCIe Tensor Core GPUs and 3rd Gen AMD EPYC™ processors, improve the performance and cost-effectiveness of a variety of GPU performance-bound real-world AI training and inferencing workloads. Use the NC A100 v4 series for workloads like object detection, video processing, image classification, speech recognition, recommender, autonomous driving reinforcement learning, oil and gas reservoir simulation, finance document parsing, web inferencing, and more.

For AI training workloads, customers will experience between 1.5 to 3.5 times the performance boost compared to the previous NC generation (NCv3) with NVIDIA Volta architecture-based GPUs. Similar performance applies to AI Inference workloads.  Moreover, customers will experience between 1.5 to five times performance boost with seven independent GPU instances on a single A100 GPU through the multi-instance GPU (MIG) feature. Customers will experience increased performance gains with smaller batch sizes.

Increased GPU VM performance instances and more importantly, greater control over the GPU resources, means our customers can gain insights faster, innovate faster, and better utilize their available resources to do more with less. This means companies can diagnose cancer faster to save more lives, provide richer, more realistic virtual worlds for people to explore, and accelerate discoveries of cures for future potential communicable diseases.

The NC A100 v4-series offers three classes of virtual machines, ranging from one to four NVIDIA A100 80GB PCIe Tensor Core GPUs. It is more cost-effective than ever before, while still giving customers the options and flexibility they need for their workloads. We can’t wait to see what you’ll build, analyze, and discover with the new Azure NC A100 v4 platform.

Learn more

Take a look at our Azure Documentation on NC A100 v4-series virtual machines.   
See additional information on performance.
Find out more about high-performance computing in Azure.
Learn why you should use an AI-first infrastructure for AI workloads.
Read about our most recent MLPerf inferencing v2.0 results.

Quelle: Azure