cloud computing
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Dispense with the chasm? No way!
Jeff Bussgang, a co-founder and general partner at Flybridge Capital, recently wrote an Extra Crunch guest post that argued it is time for a refresh when it comes to the technology adoption life cycle and the chasm. His argument went as follows:
- VCs in recent years have drastically underestimated the size of SAMs (serviceable addressable markets) for their startup investments because they were “trained to think only a portion of the SAM is obtainable within any reasonable window of time because of the chasm.”
- The chasm is no longer the barrier it once was because businesses have finally understood that software is eating the world.
- As a result, the early majority has joined up with the innovators and early adopters to create an expanded early market. Effectively, they have defected from the mainstream market to cross the chasm in the other direction, leaving only the late majority and the laggards on the other side.
- That is why we now are seeing multiple instances of very large high-growth markets that appear to have no limit to their upside. There is no chasm to cross until much later in the life cycle, and it isn’t worth much effort to cross it then.
Now, I agree with Jeff that we are seeing remarkable growth in technology adoption at levels that would have astonished investors from prior decades. In particular, I agree with him when he says:
The pandemic helped accelerate a global appreciation that digital innovation was no longer a luxury but a necessity. As such, companies could no longer wait around for new innovations to cross the chasm. Instead, everyone had to embrace change or be exposed to an existential competitive disadvantage.
But this is crossing the chasm! Pragmatic customers are being forced to adopt because they are under duress. It is not that they buy into the vision of software eating the world. It is because their very own lunches are being eaten. The pandemic created a flotilla of chasm-crossings because it unleashed a very real set of existential threats.
The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority).
The key here is to understand the difference between two buying decision processes, one governed by visionaries and technology enthusiasts (the early adopters and innovators), the other by pragmatists (the early majority). The early group makes their decisions based on their own analyses. They do not look to others for corroborative support. Pragmatists do. Indeed, word-of-mouth endorsements are by far the most impactful input not only about what to buy and when but also from whom.
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Vercel raises $102M Series C for its front-end development platform
Vercel, the company behind the popular open-source Next.js React framework, today announced that it has raised a $102 million Series C funding round led by Bedrock Capital. Existing investors Accel, CRV, Geodesic Capital, Greenoaks Capital and GV also participated in this round, together with new investors 8VC, Flex Capital, GGV, Latacora, Salesforce Ventures and Tiger Global. In total, the company has now raised $163 million and its current valuation is $1.1 billion.
As Vercel notes, the company saw strong growth in recent months, with traffic to all sites and apps on its network doubling since October 2020. The number of sites among the world’s largest 10,000 websites that use Next.js grew 50% in the same time frame, too.
Image Credits: Vercel
Given the open-source nature of the Next.js framework, not all of these users are obviously Vercel customers, but its current paying customers include the likes of Carhartt, Github, IBM, McDonald’s and Uber.
“For us, it all starts with a front-end developer,” Vercel CEO Guillermo Rauch told me. “Our goal is to create and empower those developers — and their teams — to create delightful, immersive web experiences for their customers.”
With Vercel, Rauch and his team took the Next.js framework and then built a serverless platform that specifically caters to this framework and allows developers to focus on building their front ends without having to worry about scaling and performance.
Older solutions, Rauch argues, were built in isolation from the cloud platforms and serverless technologies, leaving it up to the developers to deploy and scale their solutions. And while some potential users may also be content with using a headless content management system, Rauch argues that increasingly, developers need to be able to build solutions that can go deeper than the off-the-shelf solutions that many businesses use today.
Rauch also noted that developers really like Vercel’s ability to generate a preview URL for a site’s front end every time a developer edits the code. “So instead of just spending all your time in code review, we’re shifting the equation to spending your time reviewing or experiencing your front end. That makes the experience a lot more collaborative,” he said. “So now, designers, marketers, IT, CEOs […] can now come together in this collaboration of building a front end and say, ‘that shade of blue is not the right shade of blue.’”
“Vercel is leading a market transition through which we are seeing the majority of value-add in web and cloud application development being delivered at the front end, closest to the user, where true experiences are made and enjoyed,” said Geoff Lewis, founder and managing partner at Bedrock. “We are extremely enthusiastic to work closely with Guillermo and the peerless team he has assembled to drive this revolution forward and are very pleased to have been able to co-lead this round.”
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Vantage raises $4M to help businesses understand their AWS costs
Vantage, a service that helps businesses analyze and reduce their AWS costs, today announced that it has raised a $4 million seed round led by Andreessen Horowitz. A number of angel investors, including Brianne Kimmel, Julia Lipton, Stephanie Friedman, Calvin French Owen, Ben and Moisey Uretsky, Mitch Wainer and Justin Gage, also participated in this round.
Vantage started out with a focus on making the AWS console a bit easier to use — and helping businesses figure out what they are spending their cloud infrastructure budgets on in the process. But as Vantage co-founder and CEO Ben Schaechter told me, it was the cost transparency features that really caught on with users.
“We were advertising ourselves as being an alternative AWS console with a focus on developer experience and cost transparency,” he said. “What was interesting is — even in the early days of early access before the formal GA launch in January — I would say more than 95% of the feedback that we were getting from customers was entirely around the cost features that we had in Vantage.”
Image Credits: Vantage
Like any good startup, the Vantage team looked at this and decided to double down on these features and highlight them in its marketing, though it kept the existing AWS Console-related tools as well. The reason the other tools didn’t quite take off, Schaechter believes, is because more and more, AWS users have become accustomed to infrastructure-as-code to do their own automatic provisioning. And with that, they spend a lot less time in the AWS Console anyway.
“But one consistent thing — across the board — was that people were having a really, really hard time 12 times a year, where they would get a shocking AWS bill and had to figure out what happened. What Vantage is doing today is providing a lot of value on the transparency front there,” he said.
Over the course of the last few months, the team added a number of new features to its cost transparency tools, including machine learning-driven predictions (both on the overall account level and service level) and the ability to share reports across teams.
Image Credits: Vantage
While Vantage expects to add support for other clouds in the future, likely starting with Azure and then GCP, that’s actually not what the team is focused on right now. Instead, Schaechter noted, the team plans to add support for bringing in data from third-party cloud services instead.
“The number one line item for companies tends to be AWS, GCP, Azure,” he said. “But then, after that, it’s Datadog, Cloudflare, Sumo Logic, things along those lines. Right now, there’s no way to see, P&L or an ROI from a cloud usage-based perspective. Vantage can be the tool where that’s showing you essentially, all of your cloud costs in one space.”
That is likely the vision the investors bought into, as well, and even though Vantage is now going up against enterprise tools like Apptio’s Cloudability and VMware’s CloudHealth, Schaechter doesn’t seem to be all that worried about the competition. He argues that these are tools that were born in a time when AWS had only a handful of services and only a few ways of interacting with those. He believes that Vantage, as a modern self-service platform, will have quite a few advantages over these older services.
“You can get up and running in a few clicks. You don’t have to talk to a sales team. We’re helping a large number of startups at this stage all the way up to the enterprise, whereas Cloudability and CloudHealth are, in my mind, kind of antiquated enterprise offerings. No startup is choosing to use those at this point, as far as I know,” he said.
The team, which until now mostly consisted of Schaechter and his co-founder and CTO Brooke McKim, bootstrapped the company up to this point. Now they plan to use the new capital to build out its team (and the company is actively hiring right now), both on the development and go-to-market side.
The company offers a free starter plan for businesses that track up to $2,500 in monthly AWS cost, with paid plans starting at $30 per month for those who need to track larger accounts.
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Esper raises $30M Series B for its IoT DevOps platform
There may be billions of IoT devices in use today, but the tooling around building (and updating) the software for them still leaves a lot to be desired. Esper, which today announced that it has raised a $30 million Series B round, builds the tools to enable developers and engineers to deploy and manage fleets of Android-based edge devices. The round was led by Scale Venture Partners, with participation from Madrona Venture Group, Root Ventures, Ubiquity Ventures and Haystack.
The company argues that there are thousands of device manufacturers who are building these kinds of devices on Android alone, but that scaling and managing these deployments comes with a lot of challenges. The core idea here is that Esper brings to device development the DevOps experience that software developers now expect. The company argues that its tools allow companies to forgo building their own internal DevOps teams and instead use its tooling to scale their Android-based IoT fleets for use cases that range from digital signage and kiosks to custom solutions in healthcare, retail, logistics and more.
“The pandemic has transformed industries like connected fitness, digital health, hospitality, and food delivery, further accelerating the adoption of intelligent edge devices. But with each new use case, better software automation is required,” said Esper CEO and co-founder Yadhu Gopalan, who founded the company together with COO Shiv Sundar. “Esper’s mature cloud infrastructure incorporates the functionality cloud developers have come to expect, re-imagined for devices.”
Image Credits: Esper
Mobile device management (MDM) isn’t exactly a new thing, but the Esper team argues that these tools weren’t created for this kind of use case. “MDMs are the solution now in the market. They are made for devices being brought into an environment,” Gopalan said. “The DNA of these solutions is rooted in protecting the enterprise and to deploy applications to them in the network. Our customers are sending devices out into the wild. It’s an entirely different use case and model.”
To address these challenges, Esper offers a range of tools and services that includes a full development stack for developers, cloud-based services for device management and hardware emulators to get started with building custom devices.
“Esper helped us launch our Fusion-connected fitness offering on three different types of hardware in less than six months,” said Chris Merli, founder at Inspire Fitness. “Their full stack connected fitness Android platform helped us test our application on different hardware platforms, configure all our devices over the cloud, and manage our fleet exactly to our specifications. They gave us speed, Android expertise, and trust that our application would provide a delightful experience for our customers.”
The company also offers solutions for running Android on older x86 Windows devices to extend the life of this hardware, too.
“We spent about a year and a half on building out the infrastructure,” said Gopalan. “Definitely. That’s the hard part and that’s really creating a reliable, robust mechanism where customers can trust that the bits will flow to the devices. And you can also roll back if you need to.”
Esper is working with hardware partners to launch devices that come with built-in Esper-support from the get-go.
Esper says it saw 70x revenue growth in the last year, an 8x growth in paying customers and a 15x growth in devices running Esper. Since we don’t know the baseline, those numbers are meaningless, but the investors clearly believe that Esper is on to something. Current customers include the likes of CloudKitchens, Spire Health, Intelity, Ordermark, Inspire Fitness, RomTech and Uber.
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Google Cloud launches Vertex AI, a new managed machine learning platform
At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. It’s a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesn’t traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.
The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. “Machine learning in the enterprise is in crisis, in my view,” Craig Wiley, the director of product management for Google Cloud’s AI Platform, told me. “As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”
Image Credits: Google
Wiley, who was also the general manager of AWS’s SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, “many of which were dead ends,” according to him (including some of Google’s own). “Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models they’re building.”
Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.
Image Credits: Google
The service is also integrated with Vizier, Google’s AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.
Vertex also offers a “Feature Store” that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.
Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Cloud’s AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.
To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and — and this may be a bit of a surprise — BigQuery ML, Google’s tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.
“We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production,” said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. “We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.”
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With $21M in funding, Code Ocean aims to help researchers replicate data-heavy science
Every branch of science is increasingly reliant on big data sets and analysis, which means a growing confusion of formats and platforms — more than inconvenient, this can hinder the process of peer review and replication of research. Code Ocean hopes to make it easier for scientists to collaborate by making a flexible, shareable format and platform for any and all data sets and methods, and it has raised a total of $21 million to build it out.
Certainly there’s an air of “Too many options? Try this one!” to this (and here’s the requisite relevant XKCD). But Code Ocean isn’t creating a competitor to successful tools like Jupyter or GitLab or Docker — it’s more of a small-scale container platform that lets you wrap up all the necessary components of your data and analysis in an easily shared format, whatever platform they live on natively.
The trouble appears when you need to share what you’re doing with another researcher, whether they’re on the bench next to you or at a university across the country. It’s important for replication purposes that data analysis — just like any other scientific technique — be done exactly the same way. But there’s no guarantee that your colleague will use the same structures, formats, notation, labels and so on.
That doesn’t mean it’s impossible to share your work, but it does add a lot of extra steps as would-be replicators or iterators check and double check that all the methods are the same, that the same versions of the same tools are being used in the same order, with the same settings, and so on. A tiny inconsistency can have major repercussions down the road.
Turns out this problem is similar in a way to how many cloud services are spun up. Software deployments can be as finicky as scientific experiments, and one solution to this is containers, which like tiny virtual machines include everything needed to accomplish a computing task, in a portable format compatible with many different setups. The idea is a natural one to transfer to the research world, where you can tie up all in one tidy package the data, the software used and the specific techniques and processes used to reach a given result. That, at least, is the pitch Code Ocean offers for its platform and “Compute Capsules.”
Image Credits: Code Ocean
Say you’re a microbiologist looking at the effectiveness of a promising compound on certain muscle cells. You’re working in R, writing in RStudio on an Ubuntu machine, and your data are such and such collected during an in vitro observation. While you would naturally declare all this when you publish, there’s no guarantee anyone has an Ubuntu laptop with a working RStudio setup around, so even if you provide all the code, it might be for nothing.
If, however, you put it on Code Ocean, like this, it makes all the relevant code available, and capable of being inspected and run unmodified with a click, or being fiddled with if a colleague is wondering about a certain piece. It works through a single link and web app, cross platform, and can even be embedded on a webpage like a document or video. (I’m going to try to do that below, but our backend is a little finicky. The capsule itself is here.)
More than that, though, the Compute Capsule can be repurposed by others with new data and modifications. Maybe the technique you put online is a general purpose RNA sequence analysis tool that works as long as you feed it properly formatted data, and that’s something others would have had to code from scratch in order to take advantage of some platforms.
Well, they can just clone your capsule, run it with their own data and get their own results in addition to verifying your own. This can be done via the Code Ocean website or just by downloading a zip file of the whole thing and getting it running on their own computer, if they happen to have a compatible setup. A few more example capsules can be found here.
Image Credits: Code Ocean
This sort of cross-pollination of research techniques is as old as science, but modern data-heavy experimentation often ends up siloed because it can’t easily be shared and verified even though the code is technically available. That means other researchers move on, build their own thing and further reinforce the silo system.
Right now there are about 2,000 public compute capsules on Code Ocean, most of which are associated with a published paper. Most have also been used by others, either to replicate or try something new, and some, like ultra-specific open source code libraries, have been used by thousands.
Naturally there are security concerns when working with proprietary or medically sensitive data, and the enterprise product allows the whole system to run on a private cloud platform. That way it would be more of an internal tool, and at major research institutions that in itself could be quite useful.
Code Ocean hopes that by being as inclusive as possible in terms of codebases, platforms, compute services and so on will make for a more collaborative environment at the cutting edge.
Clearly that ambition is shared by others, as the the company has raised $21 million so far, $6 million of which was in previously undisclosed investments and $15 million in an A round announced today. The A round was led by Battery Ventures, with Digitalis Ventures, EBSCO and Vaal Partners participating as well as numerous others.
The money will allow the company to further develop, scale and promote its platform. With luck they’ll soon find themselves among the rarefied air often breathed by this sort of savvy SaaS — necessary, deeply integrated and profitable.
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The health data transparency movement is birthing a new generation of startups
In the early 2000s, Jeff Bezos gave a seminal TED Talk titled “The Electricity Metaphor for the Web’s Future.” In it, he argued that the internet will enable innovation on the same scale that electricity did.
We are at a similar inflection point in healthcare, with the recent movement toward data transparency birthing a new generation of innovation and startups.
Those who follow the space closely may have noticed that there are twin struggles taking place: a push for more transparency on provider and payer data, including anonymous patient data, and another for strict privacy protection for personal patient data. What’s the main difference?
This sector is still somewhat nascent — we are in the first wave of innovation, with much more to come.
Anonymized data is much more freely available, while personal data is being locked even tighter (as it should be) due to regulations like GDPR, CCPA and their equivalents around the world.
The former trend is enabling a host of new vendors and services that will ultimately make healthcare better and more transparent for all of us.
These new companies could not have existed five years ago. The Affordable Care Act was the first step toward making anonymized data more available. It required healthcare institutions (such as hospitals and healthcare systems) to publish data on costs and outcomes. This included the release of detailed data on providers.
Later legislation required biotech and pharma companies to disclose monies paid to research partners. And every physician in the U.S. is now required to be in the National Practitioner Identifier (NPI), a comprehensive public database of providers.
All of this allowed the creation of new types of companies that give both patients and providers more control over their data. Here are some key examples of how.
Allowing patients to access all their own health data in one place
This is a key capability of patients’ newly found access to health data. Think of how often, as a patient, providers aren’t aware of treatment or a test you’ve had elsewhere. Often you end up repeating a test because a provider doesn’t have a record of a test conducted elsewhere.
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DigitalOcean says customer billing data accessed in data breach
DigitalOcean has emailed customers warning of a data breach involving customers’ billing data, TechCrunch has learned.
The cloud infrastructure giant told customers in an email on Wednesday, obtained by TechCrunch, that it has “confirmed an unauthorized exposure of details associated with the billing profile on your DigitalOcean account.” The company said the person “gained access to some of your billing account details through a flaw that has been fixed” over a two-week window between April 9 and April 22.
The email said customer billing names and addresses were accessed, as well as the last four digits of the payment card, its expiry date and the name of the card-issuing bank. The company said that customers’ DigitalOcean accounts were “not accessed,” and passwords and account tokens were “not involved” in this breach.
“To be extra careful, we have implemented additional security monitoring on your account. We are expanding our security measures to reduce the likelihood of this kind of flaw occuring [sic] in the future,” the email said.
DigitalOcean said it fixed the flaw and notified data protection authorities, but it’s not clear what the apparent flaw was that put customer billing information at risk.
In a statement, DigitalOcean’s security chief Tyler Healy said 1% of billing profiles were affected by the breach, but declined to address our specific questions, including how the vulnerability was discovered and which authorities have been informed.
Companies with customers in Europe are subject to GDPR and can face fines of up to 4% of their global annual revenue.
Last year, the cloud company raised $100 million in new debt, followed by another $50 million round, months after laying off dozens of staff amid concerns about the company’s financial health. In March, the company went public, raising about $775 million in its initial public offering.
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5 emerging use cases for productivity infrastructure in 2021
When the world flipped upside down last year, nearly every company in every industry was forced to implement a remote workforce in just a matter of days — they had to scramble to ensure employees had the right tools in place and customers felt little to no impact. While companies initially adopted solutions for employee safety, rapid response and short-term air cover, they are now shifting their focus to long-term, strategic investments that empower growth and streamline operations.
As a result, categories that make up productivity infrastructure — cloud communications services, API platforms, low-code development tools, business process automation and AI software development kits — grew exponentially in 2020. This growth was boosted by an increasing number of companies prioritizing tools that support communication, collaboration, transparency and a seamless end-to-end workflow.
Productivity infrastructure is on the rise and will continue to be front and center as companies evaluate what their future of work entails and how to maintain productivity, rapid software development and innovation with distributed teams.
According to McKinsey & Company, the pandemic accelerated the share of digitally enabled products by seven years, and “the digitization of customer and supply-chain interactions and of internal operations by three to four years.” As demand continues to grow, companies are taking advantage of the benefits productivity infrastructure brings to their organization both internally and externally, especially as many determine the future of their work.
Automate workflows and mitigate risk
Developers rely on platforms throughout the software development process to connect data, process it, increase their go-to-market velocity and stay ahead of the competition with new and existing products. They have enormous amounts of end-user data on hand, and productivity infrastructure can remove barriers to access, integrate and leverage this data to automate the workflow.
Access to rich interaction data combined with pre-trained ML models, automated workflows and configurable front-end components enables developers to drastically shorten development cycles. Through enhanced data protection and compliance, productivity infrastructure safeguards critical data and mitigates risk while reducing time to ROI.
As the post-pandemic workplace begins to take shape, how can productivity infrastructure support enterprises where they are now and where they need to go next?
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Google’s Anthos multicloud platform gets improved logging, Windows container support and more
Google today announced a sizable update to its Anthos multicloud platform that lets you build, deploy and manage containerized applications anywhere, including on Amazon’s AWS and (in preview) on Microsoft Azure.
Version 1.7 includes new features like improved metrics and logging for Anthos on AWS, a new Connect gateway to interact with any cluster right from Google Cloud and a preview of Google’s managed control plane for Anthos Service Mesh. Other new features include Windows container support for environments that use VMware’s vSphere platform and new tools for developers to make it easier for them to deploy their applications to any Anthos cluster.
Today’s update comes almost exactly two years after Google CEO Sundar Pichai originally announced Anthos at its Cloud Next event in 2019 (before that, Google called this project the “Google Cloud Services Platform,” which launched three years ago). Hybrid and multicloud, it’s fair to say, takes a key role in the Google Cloud roadmap — and maybe more so for Google than for any of its competitors. Recently, Google brought on industry veteran Jeff Reed to become the VP of Product Management in charge of Anthos.
Reed told me that he believes that there are a lot of factors right now that are putting Anthos in a good position. “The wind is at our back. We bet on Kubernetes, bet on containers — those were good decisions,” he said. Increasingly, customers are also now scaling out their use of Kubernetes and have to figure out how to best scale out their clusters and deploy them in different environments — and to do so, they need a consistent platform across these environments. He also noted that when it comes to bringing on new Anthos customers, it’s really those factors that determine whether a company will look into Anthos or not.
He acknowledged that there are other players in this market, but he argues that Google Cloud’s take on this is also quite different. “I think we’re pretty unique in the sense that we’re from the cloud, cloud-native is our core approach,” he said. “A lot of what we talk about in [Anthos] 1.7 is about how we leverage the power of the cloud and use what we call “an anchor in the cloud” to make your life much easier. We’re more like a cloud vendor there, but because we support on-prem, we see some of those other folks.” Those other folks being IBM/Red Hat’s OpenShift and VMware’s Tanzu, for example.
The addition of support for Windows containers in vSphere environments also points to the fact that a lot of Anthos customers are classical enterprises that are trying to modernize their infrastructure, yet still rely on a lot of legacy applications that they are now trying to bring to the cloud.
Looking ahead, one thing we’ll likely see is more integrations with a wider range of Google Cloud products into Anthos. And indeed, as Reed noted, inside of Google Cloud, more teams are now building their products on top of Anthos themselves. In turn, that then makes it easier to bring those services to an Anthos-managed environment anywhere. One of the first of these internal services that run on top of Anthos is Apigee. “Your Apigee deployment essentially has Anthos underneath the covers. So Apigee gets all the benefits of a container environment, scalability and all those pieces — and we’ve made it really simple for that whole environment to run kind of as a stack,” he said.
I guess we can expect to hear more about this in the near future — or at Google Cloud Next 2021.
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