cloud computing
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Stacklet raises $18M for its cloud governance platform
Stacklet, a startup that is commercializing the Cloud Custodian open-source cloud governance project, today announced that it has raised an $18 million Series A funding round. The round was led by Addition, with participation from Foundation Capital and new individual investor Liam Randall, who is joining the company as VP of business development. Addition and Foundation Capital also invested in Stacklet’s seed round, which the company announced last August. This new round brings the company’s total funding to $22 million.
Stacklet helps enterprises manage their data governance stance across different clouds, accounts, policies and regions, with a focus on security, cost optimization and regulatory compliance. The service offers its users a set of pre-defined policy packs that encode best practices for access to cloud resources, though users can obviously also specify their own rules. In addition, Stacklet offers a number of analytics functions around policy health and resource auditing, as well as a real-time inventory and change management logs for a company’s cloud assets.
The company was co-founded by Travis Stanfield (CEO) and Kapil Thangavelu (CTO). Both bring a lot of industry expertise to the table. Stanfield spent time as an engineer at Microsoft and leading DealerTrack Technologies, while Thangavelu worked at Canonical and most recently in Amazon’s AWSOpen team. Thangavelu is also one of the co-creators of the Cloud Custodian project, which was first incubated at Capital One, where the two co-founders met during their time there, and is now a sandbox project under the Cloud Native Computing Foundation’s umbrella.
“When I joined Capital One, they had made the executive decision to go all-in on cloud and close their data centers,” Thangavelu told me. “I got to join on the ground floor of that movement and Custodian was born as a side project, looking at some of the governance and security needs that large regulated enterprises have as they move into the cloud.”
As companies have sped up their move to the cloud during the pandemic, the need for products like Stacklets has also increased. The company isn’t naming most of its customers, but it has disclosed FICO a design partner. Stacklet isn’t purely focused on the enterprise, though. “Once the cloud infrastructure becomes — for a particular organization — large enough that it’s not knowable in a single person’s head, we can deliver value for you at that time and certainly, whether it’s through the open source or through Stacklet, we will have a story there.” The Cloud Custodian open-source project is already seeing serious use among large enterprises, though, and Stacklet obviously benefits from that as well.
“In just 8 months, Travis and Kapil have gone from an idea to a functioning team with 15 employees, signed early Fortune 2000 design partners and are well on their way to building the Stacklet commercial platform,” Foundation Capital’s Sid Trivedi said. “They’ve done all this while sheltered in place at home during a once-in-a-lifetime global pandemic. This is the type of velocity that investors look for from an early-stage company.”
Looking ahead, the team plans to use the new funding to continue to developed the product, which should be generally available later this year, expand both its engineering and its go-to-market teams and continue to grow the open-source community around Cloud Custodian.
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Supabase raises $6M for its open-source Firebase alternative
Supabase, a YC-incubated startup that offers developers an open-source alternative to Google’s Firebase and similar platforms, today announced that it has raised a $6 million funding round led by Coatue, with participation from YC, Mozilla and a group of about 20 angel investors.
Currently, Supabase includes support for PostgreSQL databases and authentication tools, with a storage and serverless solution coming soon. It currently provides all the usual tools for working with databases — and listening to database changes — as well as a web-based UI for managing them. The team is quick to note that while the comparison with Google’s Firebase is inevitable, it is not meant to be a 1-to-1 replacement for it. And unlike Firebase, which uses a NoSQL database, Supabase is using PostgreSQL.
Indeed, the team relies heavily on existing open-source projects and contributes to them where it can. One of Supabase’s full-time employees maintains the PostgREST tool for building APIs on top of the database, for example.
“We’re not trying to build another system,” Supabase co-founder and CEO Paul Copplestone told me. “We just believe that already there are well-trusted, scalable enterprise open-source products out there and they just don’t have this usability component. So actually right now, Supabase is an amalgamation of six tools, soon to be seven. Some of them we built ourselves. If we go to market and can’t find anything that we think is going to be scalable — or really solve the problems — then we’ll build it and we’ll open-source it. But otherwise, we’ll use existing tools.”
Image Credits: Supabase
The traditional route to market for open-source tools is to create a tool and then launch a hosted version — maybe with some additional features — to monetize the work. Supabase took a slightly different route and launched a hosted version right away.
If somebody would want to host the service themselves, the code is available, but running your own PaaS is obviously a major challenge, but that’s also why the team went with this approach. What you get with Firebase, he noted, is that it’s a few clicks to set everything up. Supabase wanted to be able to offer the same kind of experience. “That’s one thing that self-hosting just cannot offer,” he said. “You can’t really get the same wow factor that you can if we offered a hosted platform where you literally [have] one click and then a couple of minutes later, you’ve got everything set up.”
In addition, he also noted that he wanted to make sure the company could support the growing stable of tools it was building and commercializing its tools based on its database services was the easiest way to do so.
Like other Y Combinator startups, Supabase closed its funding round after the accelerator’s demo day in August. The team had considered doing a SAFE round, but it found the right group of institutional investors that offered founder-friendly terms to go ahead with this institutional round instead.
“It’s going to cost us a lot to compete with the generous free tier that Firebase offers,” Copplestone said. “And it’s databases, right? So it’s not like you can just keep them stateless and shut them down if you’re not really using them. [This funding round] gives us a long, generous runway and more importantly, for the developers who come in and build on top of us, [they can] take as long as they want and then start monetizing later on themselves.“
The company plans to use the new funding to continue to invest in its various tools and hire to support its growth.
“Supabase’s value proposition of building in a weekend and scaling so quickly hit home immediately,” said Caryn Marooney, general partner at Coatue and Facebook’s former VP of Global Communications. “We are proud to work with this team, and we are excited by their laser focus on developers and their commitment to speed and reliability.”
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AWS expands on SageMaker capabilities with end-to-end features for machine learning
Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.
As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.
“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”
Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.
The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.
Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.
Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.
To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.
Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.
Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.
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SingleStore, formerly MemSQL, raises $80M to integrate and leverage companies’ disparate data silos
While the enterprise world likes to talk about “big data”, that term belies the real state of how data exists for many organizations: the truth of the matter is that it’s often very fragmented, living in different places and on different systems, making the concept of analysing and using it in a single, effective way a huge challenge.
Today, one of the big up-and-coming startups that has built a platform to get around that predicament is announcing a significant round of funding, a sign of the demand for its services and its success so far in executing on that.
SingleStore, which provides a SQL-based platform to help enterprises manage, parse and use data that lives in silos across multiple cloud and on-premise environments — a key piece of work needed to run applications in risk, fraud prevention, customer user experience, real-time reporting and real-time insights, fast dashboards, data warehouse augmentation, modernization for data warehouses and data architectures and faster insights — has picked up $80 million in funding, a Series E round that brings in new strategic investors alongside its existing list of backers.
The round is being led by Insight Partners, with new backers Dell Technologies Capital, Hercules Capital; and previous backers Accel, Anchorage, Glynn Capital, GV (formerly Google Ventures) and Rev IV also participating.
Alongside the investment, SingleStore is formally announcing a new partnership with analytics powerhouse SAS. I say “formally” because they two have been working together already and it’s resulted in “tremendous uptake,” CEO Raj Verma said in an interview over email.
Verma added that the round came out of inbound interest, not its own fundraising efforts, and as such, it brings the total amount of cash it has on hand to $140 million. The gives the startup money to play with not only to invest in hiring, R&D and business development, but potentially also M&A, given that the market right now seems to be in a period of consolidation.
Verma said the valuation is a “significant upround” compared to its Series D in 2018 but didn’t disclose the figure. PitchBook notes that at the time it was valued at $270 million post-money.
When I last spoke with the startup in May of this year — when it announced a debt facility of $50 million — it was not called SingleStore; it was MemSQL. The company rebranded at the end of October to the new name, but Verma said that the change was a long time in the planning.
“The name change is one of the first conversations I had when I got here,” he said about when he joined the company in 2019 (he’s been there for about 16 months). “The [former] name didn’t exactly flow off the tongue and we found that it no longer suited us, we found ourselves in a tiny shoebox of an offering, in saying our name is MemSQL we were telling our prospects to think of us as in-memory and SQL. SQL we didn’t have a problem with but we had outgrown in-memory years ago. That was really only 5% of our current revenues.”
He also mentioned the hang up many have with in-memory database implementations: they tend to be expensive. “So this implied high TCO, which couldn’t have been further from the truth,” he said. “Typically we are ⅕-⅛ the cost of what a competitive product would be to implement. We were doing ourselves a disservice with prospects and buyers.”
The company liked the name SingleStore because it is based a conceptual idea of its proprietary technology. “We wanted a name that could be a verb. Down the road we hope that when someone asks large enterprises what they do with their data, they will say that they ‘SingleStore It!’ That is the vision. The north star is that we can do all types of data without workload segmentation,” he said.
That effort is being done at a time when there is more competition than ever before in the space. Others also providing tools to manage and run analytics and other work on big data sets include Amazon, Microsoft, Snowflake, PostgreSQL, MySQL and more.
SingleStore is not disclosing any metrics on its growth at the moment but says it has thousands of enterprise customers. Some of the more recent names it’s disclosed include GE, IEX Cloud, Go Guardian, Palo Alto Networks, EOG Resources, SiriusXM + Pandora, with partners including Infosys, HCL and NextGen.
“As industry after industry reinvents itself using software, there will be accelerating market demand for predictive applications that can only be powered by fast, scalable, cloud-native database systems like SingleStore’s,” said Lonne Jaffe, managing director at Insight Partners, in a statement. “Insight Partners has spent the past 25 years helping transformational software companies rapidly scale-up, and we’re looking forward to working with Raj and his management team as they bring SingleStore’s highly differentiated technology to customers and partners across the world.”
“Across industries, SAS is running some of the most demanding and sophisticated machine learning workloads in the world to help organizations make the best decisions. SAS continues to innovate in AI and advanced analytics, and we partner with companies like SingleStore that share our curiosity about how data and analytics can help organizations reimagine their businesses and change the world,” said Oliver Schabenberger, COO and CTO at SAS, added. “Our engineering teams are integrating SingleStore’s scalable SQL-based database platform with the massively parallel analytics engine SAS Viya. We are excited to work with SingleStore to improve performance, reduce cost, and enable our customers to be at the forefront of analytics and decisioning.”
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3 ways the pandemic is transforming tech spending
Ever since the pandemic hit the U.S. in full force last March, the B2B tech community keeps asking the same questions: Are businesses spending more on technology? What’s the money getting spent on? Is the sales cycle faster? What trends will likely carry into 2021?
Recently we decided to join forces to answer these questions. We analyzed data from the just-released Q4 2020 Outlook of the Coupa Business Spend Index (BSI), a leading indicator of economic growth, in light of hundreds of conversations we have had with business-tech buyers this year.
A former Battery Ventures portfolio company, Coupa* is a business spend-management company that has cumulatively processed more than $2 trillion in business spending. This perspective gives Coupa unique, real-time insights into tech spending trends across multiple industries.
Tech spending is continuing despite the economic recession — which helps explain why many startups are raising large rounds and even tapping public markets for capital.
Broadly speaking, tech spending is continuing despite the economic recession — which helps explain why many tech startups are raising large financing rounds and even tapping the public markets for capital. Here are our three specific takeaways on current tech spending:
Spending is shifting away from remote collaboration to SaaS and cloud computing
Tech spending ranks among the hottest boardroom topics today. Decisions that used to be confined to the CIO’s organization are now operationally and strategically critical to the CEO. Multiple reasons drive this shift, but the pandemic has forced businesses to operate and engage with customers differently, almost overnight. Boards recognize that companies must change their business models and operations if they don’t want to become obsolete. The question on everyone’s mind is no longer “what are our technology investments?” but rather, “how fast can they happen?”
Spending on WFH/remote collaboration tools has largely run its course in the first wave of adaptation forced by the pandemic. Now we’re seeing a second wave of tech spending, in which enterprises adopt technology to make operations easier and simply keep their doors open.
SaaS solutions are replacing unsustainable manual processes. Consider Rhode Island’s decision to shift from in-person citizen surveying to using SurveyMonkey. Many companies are shifting their vendor payments to digital payments, ditching paper checks entirely. Utility provider PG&E is accelerating its digital transformation roadmap from five years to two years.
The second wave of adaptation has also pushed many companies to embrace the cloud, as this chart makes clear:
Image Credits: Battery Ventures (opens in a new window)
Similarly, the difficulty of maintaining a traditional data center during a pandemic has pushed many companies to finally shift to cloud infrastructure under COVID. As they migrate that workload to the cloud, the pie is still expanding. Goldman Sachs and Battery Ventures data suggest $600 billion worth of disruption potential will bleed into 2021 and beyond.
In addition to SaaS and cloud adoption, companies across sectors are spending on technologies to reduce their reliance on humans. For instance, Tyson Foods is investing in and accelerating the adoption of automated technology to process poultry, pork and beef.
All companies are digital product companies now
Mention “digital product company” in the past, and we’d all think of Netflix. But now every company has to reimagine itself as offering digital products in a meaningful way.
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Google acquires Actifio to step into the area of data management and business continuity
In the same week that Amazon is holding its big AWS confab, Google is also announcing a move to raise its own enterprise game with Google Cloud. Today the company announced that it is acquiring Actifio, a data management company that helps companies with data continuity to be better prepared in the event of a security breach or other need for disaster recovery. The deal squares Google up as a competitor against the likes of Rubrik, another big player in data continuity.
The terms of the deal were not disclosed in the announcement; we’re looking and will update as we learn more. Notably, when the company was valued at over $1 billion in a funding round back in 2014, it had said it was preparing for an IPO (which never happened). PitchBook data estimated its value at $1.3 billion in 2018, but earlier this year it appeared to be raising money at about a 60% discount to its recent valuation, according to data provided to us by Prime Unicorn Index.
The company was also involved in a patent infringement suit against Rubrik, which it also filed earlier this year.
It had raised around $461 million, with investors including Andreessen Horowitz, TCV, Tiger, 83 North, and more.
With Actifio, Google is moving into what is one of the key investment areas for enterprises in recent years. The growth of increasingly sophisticated security breaches, coupled with stronger data protection regulation, has given a new priority to the task of holding and using business data more responsibly, and business continuity is a cornerstone of that.
Google describes the startup as as a “leader in backup and disaster recovery” providing virtual copies of data that can be managed and updated for storage, testing, and more. The fact that it covers data in a number of environments — including SAP HANA, Oracle, Microsoft SQL Server, PostgreSQL, and MySQL, virtual machines (VMs) in VMware, Hyper-V, physical servers, and of course Google Compute Engine — means that it also gives Google a strong play to work with companies in hybrid and multi-vendor environments rather than just all-Google shops.
“We know that customers have many options when it comes to cloud solutions, including backup and DR, and the acquisition of Actifio will help us to better serve enterprises as they deploy and manage business-critical workloads, including in hybrid scenarios,” writes Brad Calder, VP, engineering, in the blog post. :In addition, we are committed to supporting our backup and DR technology and channel partner ecosystem, providing customers with a variety of options so they can choose the solution that best fits their needs.”
The company will join Google Cloud.
“We’re excited to join Google Cloud and build on the success we’ve had as partners over the past four years,” said Ash Ashutosh, CEO at Actifio, in a statement. “Backup and recovery is essential to enterprise cloud adoption and, together with Google Cloud, we are well-positioned to serve the needs of data-driven customers across industries.”
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Abacus.AI raises another $22M and launches new AI modules
AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.
Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI
In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.
“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.
Image Credits: Abacus.AI
Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.
Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.
As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.
Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.
Image Credits: Abacus.AI
It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.
Come January, the company also plans to launch support for more languages and more machine vision use cases.
“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups,” Yanda Erlich, a p artner at Coatue Ventures told me. “Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”
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AI-tool maker Seldon raises £7.1M Series A from AlbionVC and Cambridge Innovation Capital
Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize machine learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting does actually end up working.
It has now raised a £7.1 million Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1 million funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy — a new enterprise solution — to market and double the size of the team over the next 18 months.
More accurately, Seldon is a cloud-agnostic machine learning (ML) deployment specialist which works in partnership with industry leaders such as Google, Red Hat, IBM and Amazon Web Services.
Key to its success is that its open-source project Seldon Core has more than 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.
Alex Housley, CEO and founder of Seldon speaking to TechCrunch explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”
“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.
In addition, he said Seldon’s open-source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”
Nadine Torbey, an investor at AlbionVC, added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”
Vin Lingathoti, partner at Cambridge Innovation Capital, said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring machine learning models at scale.”
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Arrikto raises $10M for its MLOps platform
Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.
“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”
Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.
Image Credits: Arrikto
“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.
Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.
Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto
At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.
As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.
While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.
And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.
“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”
Image Credits: Arrikto
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Alibaba passes IBM in cloud infrastructure market with over $2B in revenue
When Alibaba entered the cloud infrastructure market in earnest in 2015 it had ambitious goals, and it has been growing steadily. Today, the Chinese e-commerce giant announced quarterly cloud revenue of $2.194 billion. With that number, it has passed IBM’s $1.65 billion revenue result (according to Synergy Research market share numbers), a significant milestone.
But while $2 billion is a large figure, it’s one worth keeping in perspective. For example, Amazon announced $11.6 billion in cloud infrastructure revenue for its most recent quarter, while Microsoft’s Azure came in second place with $5.9 billion.
Google Cloud has held onto third place, as it has for as long as we’ve been covering the cloud infrastructure market. In its most recent numbers, Synergy pegged Google at 9% market share, or approximately $2.9 billion in revenue.
While Alibaba is still a fair bit behind Google, today’s numbers puts the company firmly in fourth place now, well ahead of IBM . It’s doubtful it could catch Google anytime soon, especially as the company has become more focused under CEO Thomas Kurian, but it is still fairly remarkable that it managed to pass IBM, a stalwart of enterprise computing for decades, as a relative newcomer to the space.
The 60% growth represented a slight increase from the previous quarter’s 59%, but basically means it held steady, something that’s not easy to do as a company reaches a certain revenue plateau. In its earnings call today, Daniel Zhang, chairman and CEO at Alibaba Group, said that in China, which remains the company’s primary market, digital transformation driven by the pandemic was a primary factor in keeping growth steady.
“Cloud is a fast-growing business. If you look at our revenue breakdown, obviously, cloud is enjoying a very, very fast growth. And what we see is that all the industries are in the process of digital transformation. And moving to the cloud is a very important step for the industries,” Zhang said in the call.
He believes eventually that most business will be done in the cloud, and the growth could continue for the medium term, as there are still many companies that haven’t made the switch yet, but will do so over time.
John Dinsdale, an analyst at Synergy Research, says that while China remains its primary market, the company does have a presence outside the country too, and can afford to play the long game in terms of the current geopolitical situation with trade tensions between the U.S. and China.
“Alibaba has already made some strides outside of China and Hong Kong. While the scale is rather small compared with its Chinese operations, Alibaba has established a data center and cloud presence in a range of countries, including six more APAC countries, U.S., U.K. and UAE. Among these, it is the market leader in both Indonesia and Malaysia,” Dinsdale told TechCrunch.
In its most recent data released a couple of weeks ago, prior to today’s numbers, Synergy broke down the market this way: “Amazon 33%, Microsoft 18%, Google 9%, Alibaba 5%, IBM 5%, Salesforce 3%, Tencent 2%, Oracle 2%, NTT 1%, SAP 1% – to the nearest percentage point.”
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