Enterprise

You shouldn’t skim over gross dollar retention • ZebethMedia

By zebethcontrolPosted on November 19, 2022Posted in customer retention, Enterprise, openview venture partners, Startups, TechCrunch Exchange newsletter, The ExchangeNo Comments

Welcome to The ZebethMedia Exchange, a weekly startups-and-markets newsletter. It’s inspired by the daily ZebethMedia+ column where it gets its name. Want it in your inbox every Saturday? Sign up here. For SaaS companies, net dollar retention is on investor radar more than ever. But it shouldn’t eclipse gross dollar retention: If you are not tracking both metrics, you could be fighting to add new customers into a leaky bucket. Let’s explore. — Anna Gross dollar retention is “what protects you during really challenging times” “Gross retention really speaks to the true stickiness and health of your customer base. It’s what protects you during really challenging times,” growth stage VC Rene Stewart said in a sponsored talk at ZebethMedia Disrupt in 2021. And yet, the co-head of Vista Equity Partners’ growth-stage Endeavor Fund added, most VCs she talked to “probably only care about net retention.” However, her comments were made in 2021, not 2022. “Challenging times” have come upon us since then, making investors and founders more mindful of business fundamentals. Alex and I have already written about the importance of net dollar retention when efficient growth is the new holy grail. But how does it differ from gross dollar retention, and how has the latter been faring at most tech companies? Let’s dive in.

Google introduces Workspaces Spaces Chats conversations summaries • ZebethMedia

By zebethcontrolPosted on November 18, 2022Posted in Enterprise, Google, Robotics & AI, summarizationNo Comments

Having trouble keeping up with the conversations in your Chats in your Workspace Spaces? Google feels your pain, and is “excited to introduce conversation summaries in Google Chat for messages in Spaces.” Now your conversations in Spaces Chats will be summarized right in your Premium Workspace. The issue is, of course, that while Chats in Spaces are perfectly good for conversations, in larger Workspaces these Chats conversations can be difficult to keep up with unless you’re always checking your Spaces for new conversations in Chats. You know the drill – you log into your Workspace, click over to your Spaces, pull up the Chats, and your conversations are just too numerous and long-winded to catch up on! You can’t very well tell your Workspace Spaces conversationalists to leave off chatting in your Chats. Conversation is the very reason Chats exist, that’s why they call it Spaces! I mean Chats! Fortunately Google is bringing its expertise in communications apps to remedy this conversational crisis in your Workspace Spaces Chats. Starting soon, the messages in your conversations will be summarized right in your Chats, inside Spaces in Workspaces! Selected Premium Workspaces, anyway. Google put a summary in your Premium Workspace Spaces Chat conversations. You read that correctly. The Conversation Summary of the messages in your Workspace Spaces Chat will appear at the top of the Chats within Spaces, summarizing any unread chatter in the Chats conversation. Click on the summary of the Spaces Chats and you’ll jump straight to the conversation, even if it’s already visible and the Conversation summary has only summarized a few lines of the Chats conversation. If you use Spaces in your Workspace, and tend to have conversations in the Chats of those Spaces, Conversation Summary in Google Chat could be just the thing to keep those chatty Chats summarized. Sadly, this doesn’t appear to be available for Google Chat, though — which is to say regular Google Chat (that is, the newish one in your Gmail that used to be Hangouts, possibly), only Google Chat for Spaces in Workspaces, and (don’t forgot) select Premium Workspaces at that. Definitely not Meet messaging. So you probably don’t have access. But you might eventually, if Workspace Spaces Chats are still something that exist in six months. (I’m checking with Google on this.) Check out the technical details on how the Google AI team quickly and effectively summarizes conversations in Chats in Spaces in Workspaces right here. 🙂

With $8.6M in seed funding, Nx wants to take monorepos mainstream • ZebethMedia

By zebethcontrolPosted on November 17, 2022Posted in Bazel, developers, Enterprise, funding, NX, StartupsNo Comments

Narwhal, the company behind the popular monorepo-focused open source Nx build system for JavaScript code, today announced that it has raised an $8.6 million seed funding round co-led by Nexus Venture Partners and Andreesen Horowitz. A number of angel investors, including GitHub co-founder Tom Preston-Werner, also participated in this round. Founded by two former Google employees on the Angular team, Jeff Cross (CEO) and Victor Savkin (CTO), Narwahl actually started out as an Angular consulting shop, helping large banks, airlines and other enterprises — the kind of companies that typically use Angular. As Cross told me, it was working with Capital One that actually pushed the team to pursue Nx and turn that into the company’s main product. At that point, the concept of monorepos was already very familiar to them, thanks to their work at Google, which uses one of the world’s largest monorepos to manage its codebase. Image Credits: Nx “They had their login team,” Cross explained. “If you logged in to CapitalOne.com, it’s seven lines of business building one unified app — and it was split across so many repositories, they couldn’t coordinate on deploys, they couldn’t coordinate really on anything. And they really needed a monorepo. And so we built Nx for their use case and then made it work with every other client we were working with, which was most of these large companies.” Cross believes that monorepos are inherently easier to manage for large teams. The founders, he said, were spoiled at Google because thanks to the monorepo, any developer could build any part of Google’s codebase with minimal effort. Everything, after all, used the same tool chain and testing infrastructure. Meanwhile, having many teams work on different repositories creates a lot of friction, given that the teams then have to build a common API — and create a new repository for it, create the integration process and figure out how to publish that. “And with publishing, inevitably every company adds versioning to the publishing. So it’s never ‘we publish every commit and it’s immediately updated in the repository.’ It’s more like: ‘we publish it, we use somewhere to say if this a breaking change, a minor one, or is this a patch? And what that ends up happening in most companies is that they never get the time to actually update it,” Cross said. So the idea behind Nx is to give every company the tools to manage their JavaScript monorepos — and migrate them to one if necessary. As Cross explained it, the open-source Nx project and Nx Cloud help companies organize their code in these massive repositories, using Nx’s concept of project graphs. It’s worth noting that Nx was great inspired by Google’s Bazel build and test system, so it includes some familiar features like the ability to distribute computation and task execution across multiple machines. Cross cited one major retail giant the company is currently working with that made the move to Nx’s enterprise product and now saves over 40,000 hours of compute time a month thanks to its distributed caching system. One of the nice features of Nx (and also Bazel, to be fair), is that it knows when two developers are trying to run the same tasks and checks if there is already a cached version. Narwhal/Nx is already a bit ahead of most open-source companies at the seed stage in that it already has a hosted service (Nx Cloud) and an enterprise version as its main products. Given the kind of large enterprise customers Nx works with, it’s no surprise that Nx offers them the ability to run the service in their private instances and isolated from external APIs. The company currently has just over 30 employees on its team, which is mostly remote. Of those, 25 are engineers. Most recently, Narwhal also took over the stewardship of Lerna.js, a popular open-source JavaScript monorepo tool that had previously remained somewhat unmaintained. Narwhal will now provide critical bug fixes and security updates for it. “Monorepo adoption is exploding worldwide, driven by advantages like ease of collaboration, shared codebase visibility, dependency management, and refactoring,” said Abhishek Sharma, managing director at Nexus Venture Partners. “However, as monorepos scale, robust tooling becomes essential to managing them, and Build Time becomes a critical factor. This is where Nx shines. We were drawn to Nx because of its world-class team, category leadership, strong developer community, and massive global adoption: from startups to Fortune 500 companies. We’re grateful to Jeff and Victor for choosing us as their partner in this journey.”

Spot AI raises $40M to build smarter CCTV security camera tech • ZebethMedia

By zebethcontrolPosted on November 17, 2022Posted in CCTV, Enterprise, spot aiNo Comments

CCTV and other kinds of security cameras have a strong big brother vibe, but for many of us that may be because we don’t really understand or know how the footage they pick up ever gets used. Today, a startup called Spot AI that’s built a system to help answer that question at least in part — it provides a cloud-based analytics system that “reads” that footage to get insights about not just security, but also safety and operational activity — is announcing $40 million in funding to grow. Scale Venture Partners is leading the round, with past backers Redpoint Ventures, Bessemer Venture Partners, and new investors StepStone Group and Modern Venture Partners also investing. This brings the total raised by Spot AI to $63 million. Spot AI, appropriately for a security camera company, existed in stealth for years before it came out into the public in 2021: at that point it had already raised $22 million. As with that round, Spot AI is not disclosing its valuation, but Tanuj Thapliyal, Spot AI’s CEO, noted that it is a “significant up-round” based on the fact that in the last 12 months, the startup’s revenues have grown fivefold, and that customers — it has “thousands” in the U.S., both small businesses and large enterprises across some 17 industries, which aren’t really those that include “knowledge workers” per se but businesses in areas like manufacturing and retail that have critical physical components and a lot of activity — have tripled in the same period. Its customers have included SpaceX, transportation company Cheeseman, Mixt and Northland Cold Storage. And it turns out that giving people a better reason for using their video cameras makes them much more interested in using and looking at that video data. “People are using our cameras a lot,” Thapliyal said in an interview. “Forty percent of our month active users log in every day. There is value to be had here.” Fundraising has gotten very challenging recently, but Thapliyal said that the San Francisco startup hasn’t seen that itself in part because of those growth numbers, and also because it’s bringing something different to market. CCTV and other security cameras have become as ubiquitous as electric lighting in many workplaces these days, especially those with high traffic. Spot AI estimates that since 2015 the number has doubled and now stands at 1 billion devices globally. In many cases, cameras are networked and link into bigger systems where footage can be viewed by security teams. But that’s typically where a lot of the usage ends, and so that is where Spot AI is hoping to pick things up. Spot AI provides a few different levels of service: for customers that already have networked cameras, they can integrate these with Spot AI’s platform so that it can start reading and parsing the video data. Those that either don’t use cameras already or that don’t have networked systems, or want the full system as envisioned by Spot AI, can potentially use free hardware built and provided by Spot AI itself. That system is based around technology that uses computer vision and other AI both in the cloud and at the edge (eg, if using Spot’s own cameras) to monitor video across security parameters, but also others around safety and efficiency and movement overall. What is monitored, and where, is set up by customers themselves by way of a drag and drop interface that lets them select specific items or areas in a frame, which the system can then analyze across a specified range of time for changes and other kinds of activity. One scenario Thapliyal described how a car wash was using its system to help resolve damage claims by pinpointing video to identify when and were, and if, damage was done during a carwash to help those claims move along. Another you could imagine could involve helping a store determine where customer assistants are spending time and where, and if they could be better positioned at different times of day. Longer term, there are some interesting opportunities for Spot AI’s platform that it’s not already pursuing, specifically in the consumer segment. Thapliyal said that selling direct to consumers — for example, building on the market created by the likes of Ring for cameras to track who comes to people’s front doors — is not one that it wants to pursue, but that there could well be an opportunity for working with businesses that in turn work with consumers. Thapliyal — who co-founded the company with Rish Gupta and Sud Bhatija — believes that with all of these, the opportunity of actually making that video useful is the route to making the video less creepy and indeed less idle. “If you make the video data [produced by these cameras] more useful and accessible to more people in the workplace, then you transform it from this idea of surveillance to the idea of video intelligence,” Thapliyal said to me in 2021. “It can help you make all sorts of important decisions.” As I said before, its ethos seems to come out of the idea that these cameras are here, so we need to find better ways of using them more effectively and responsibly. That has definitely carried the company up today and is helping shape future strategy. “For a company like ours to have impact we have to be really specific in our purpose,” Thapliyal said to me this week. One interesting scenario where Spot AI could have a place, for example, is in the area of connected cars, where carmakers might want to tap into the trend for dashcams that drivers use to help them potentially make claims in the event of accidents: many cars already have cameras built into their vehicles, but no additional ability to parse or use that video data beyond the immediate purpose of, say, helping people park. “The product usage and engagement Spot AI has seen in its customers over the past year since launch

OpsHelm emerges from stealth to automatically correct your security blunders • ZebethMedia

By zebethcontrolPosted on November 17, 2022Posted in Enterprise, OpsHelm, Security, security opsNo Comments

There are so many preventable cybersecurity incidents each year if only you were aware of the problem. It could be the classic exposed Amazon S3 bucket or a firewall vulnerability. These are what many security experts might call rookie mistakes, but which hit companies all the time because of the sheer complexity tracking security along your entire IT stack. OpsHelm, an early stage startup from a group of long-time cybersecurity professionals, wants to strip away the complexity and automatically correct a lot of the most common security mistakes, the kind that can cause big problems if they go undetected. Today, the company emerged from stealth to make the product more widely available in a public beta with GA expected early next year. “What we’re trying to do is automate a lot of what’s currently a fairly manual, interrupt-driven workflow where security tools push an alert to you. And then you’ll have to go fix the problem that they’ve identified or decide whether it’s not an issue,” company co-founder and CEO Bill Gambardella told ZebethMedia. Prior to founding OpsHelm Gambardella was COO at Leviathan Security Group, and previously ran security at Sprout Social. His three other co-founders have similar pedigrees, and that means they have experienced these kinds of issues first hand that they are trying to fix with OpsHelm. He said what he and his co-founders saw was the same mistakes and issues occurring over and over again resulting in late night or weekend meetings to try and fix a problem that could have been preventable in the first place. OpsHelm dashboard Image Credits: OpsHelm “What I saw from both ends of that spectrum was that these little misconfigurations, little cloud problems, little cloud issues, somebody innocently committed at one point, cascading into big, big problems on let’s say, Saturday night, where we all were on an all-hands-on-deck call dealing with an incident. And then you need an expensive consultancy to help you clean it up. Not an ideal place to be, but it did keep happening over and over again,” he said. OpsHelm monitors your security landscape looking for those issues, and letting you know in a common communications tool like Slack or Microsoft Teams where you can accept or reject the fix, and whatever action you take, the system learns about how to handle it next time. Gambardella says this is not based on so-called best practices so much as learning from the environment in which your company is operating, and helping teams move on without a lot of discussion, while leaving room for auditing later if it’s required. “We’re trying to move away from ‘Here’s here’s an alert you need to go investigate, drop what you’re doing, and spend 15 minutes talking to people,’ to more of ‘at 3:04 pm Tim on the Ops team, said he is OK that this S3 bucket can be on the internet and publicly exposed,’” he said. Security ops can track all of this in an operations dashboard, and could still decide to talk to the person who green lighted the exception to find out if there was a justifiable reason for this particular action, but the idea is to empower people to deal with these issues in the moment. The very stealthy startup launched earlier this year, and has raised $1.3 million seed. The

Terzo lands $16M to extract key data from contracts • ZebethMedia

By zebethcontrolPosted on November 17, 2022Posted in AI, Enterprise, funding, Robotics & AI, startup, StartupsNo Comments

Contract governance is the steps taken to make sure agreed-upon terms between a company and its suppliers are met. It’s an essential part of doing business, and the consequences for getting it wrong can be steep. McKinsey estimates that poor contract governance can cost organizations up to 9% of their total revenue, which equates to $1.4 trillion for the Fortune 500 alone and $6.4 trillion across all enterprise business-to-business companies. Challenges around contract governance have fueled the rise of startups like Icertis, which recently secured $150 million at a $3.2 billion valuation to build out its contracting tools. LinkSquares in April landed $100 million for its AI-powered contract analysis platform, while ContractPodAi, a close competitor, has raised tens of millions to digitize contract reviews. A relatively new entrant in the space is Terzo, which was co-founded by Brandon Card, Al Giocondi and Pradeep Thangavel in 2020. A suite of contract processing software, Terzo uses AI to extract data in contracts related to a company’s spend and revenue across their supplier and customer relationships. In a sign investor interest in contract management startups hasn’t waned, Terzo today closed a $16 million Series A round led by Align Ventures with participation from TYH Ventures, Engage Ventures, Human Capital and other unnamed institutional investors. The proceeds bring the company’s total raised to more than $18f million, and Card, who serves at Terzo’s CEO, says they’ll be put toward Terzo’s sales and marketing initiatives as well as enhancing the platform’s AI capabilities. “As our technology evolves, we aim to deliver advanced insights around financials and budgeting,” Card said. “Contract systems were built for legal use cases and legal teams to focus on drafting and clauses. There are no analytics or financial insights for leaders to make smarter decisions. Terzo was founded to solve that problem.” Image Credits: Terzo Card says his experiences at Microsoft, where he was an enterprise portfolio manager at Microsoft Cloud, inspired him to co-found Terzo. Giocondi came from account manager roles at Oracle and IBM. As for Thangavel, he spent nearly seven years on the engineering side at Freshworks prior to joining alongside Card and Giocondi. Card says that Terzo’s AI was trained using real-world business contracts to extract data such as inventory and costs, supervised by a quality assurance team to ensure baseline accuracy. Terzo integrates with enterprise resource platforms like SAP and Oracle to track contractual obligations and expiration dates, including metrics related to environmental, social and governance policies. “Terzo is valuable to the IT audience because it allows them to see data faster,” Card said. “We’ve created a platform that combines data management, automation and AI, but also keeps people in the loop.” VCs see promise in contract management legal tech like Terzo’s, perhaps in part because of the high customer adoption rate. According to a 2020 Bloomberg Law survey, more than half (56%) of in-house lawyers said that they’re using contract management programs. (That’s despite the fact that the legal industry is notoriously slow to adopt new tech.) If the current trend holds, Markets and Markets predicts the contract management life cycle market will grow from $1.5 billion in 2019 to $2.9 billion by 2024. Card says that Terzo has “over a dozen” customers, including a Fortune 50 retailer and the largest financial transaction processor in the world. The plan into the next year is to drive revenue at over 50% of Terzo’s overall expenses, he says, and to break even in 2024. “We are positioned to be a profitable business by 2025,” Card added. “Both the pandemic and current tech slowdown has taught us how to run a lean business that is focused on efficient growth. The current downturn has caused our customers to prioritize spending and budgeting so we look at this as a positive tailwind heading into 2023.”

Namecoach raises cash to teach users how to correctly pronounce names • ZebethMedia

By zebethcontrolPosted on November 17, 2022Posted in AI, Enterprise, funding, Robotics & AI, startup, StartupsNo Comments

We’ve all been faced with a name that’s difficult to pronounce. But not everyone considers the consequences of their mispronunciations. In a piece for Fast Company, Madhumita Mallick, the head of inclusion, equity and impact at Carta, recalls how her name became a source of anxiety even when she was a grade-school student. Studies have indeed found that when peers and teachers incorrectly pronounce or change the names of students of color, those students participate less in class and becoming socially withdrawn to avoid associating with their name. As the co-authors of one wrote in 2012: “actions and attitudes [students] experienced in K-12 schools highlight a type of cultural ‘othering’ that contradicts our goals for multicultural school environments .. even just stumbling over a name they had never seen before, the tone set by a teacher about a student’s name [is] something significant.” Praveen Shanbhag, the CEO of Namecoach, heard a speaker at his sister’s alma mater flub her name during her college graduation. That, along with his experiences as a first-generation immigrant to the U.S., inspired him to co-found Namecoach, which develops name pronunciation tools that can be embedded in existing platforms like Salesforce, Canvas and Gmail. “We are on a mission to make name mispronunciation a thing of the past for everyone,” Shanbhag told ZebethMedia via email. Shanbhag started dabbling in programming while working on his Ph.D. in philosophy at Stanford. He decided to code an app that would collect recordings of students saying their names and deliver them to name readers for graduation, which became Namecoach. By 2016, Shanbhag says that hundreds of schools were using Namecoach’s software and services. Namecoach — which eventually broadened its customer base to client brands — isn’t alone in the market. NameShouts offers a robust set of name pronunciation tools, as does Facebook, Slack and LinkedIn. But Shanbhag tells me that early on, he sought to differentiate Namecoach by investing heavily in AI and integrations. Image Credits: Namecoach For example, Namecoach uses an AI system to predict the correct pronunciation when someone’s name has multiple correct pronunciations based on factors like nationality, ethnicity, gender and location. Another of the platform’s systems synthesizes speech in situations where an audio recording of a name isn’t available. At a high level, Namecoach shows name pronunciations both user-generated and drawn from a database of audio name pronunciations. When asked about the system’s accuracy and whether users can remedy mistakes that might make their way onto the database, Shanbhag said that Namecoach consults with linguistic experts and offers a submission form for corrections. It’s not yet live, but Shanbhag says that Namecoach is developing a AI to provide pronunciations that take the speaker’s native language into account — not just the name-owner’s language. “Your name is central to your identity, and accurate pronunciation sets the tone for a positive interaction for both parties,” he said. Namecoach’s platform works out-of-the-box with services including Microsoft Teams, Outlook and Google Workspace and offers an API and software development kit to let third parties build Namecoach’s functionality into their products. But the focus over the next few months will be the startup’s first-ever consumer app, Shanbhag says, which will be made available as a Chrome extension next year. Namecoach — which today closed an $8 million Series A round led by Impact America Fund with participation from Authentic Ventures, Metaplanet, Engage.VC, Founders Fund, Bisk Ventures and others — also plans to ramp up its sales and marketing efforts and expand the platform’s collection of connectors. Shanbhag hinted at capabilities beyond pronunciation guidance coming down the pipeline, including prompts to enable warm interactions in sales scenarios and feedback to help users improve their conversations more generally. Leaning into sales applications makes sense given Namecoach’s marquee customers — Salesforce, Netjets and PwC. Beyond those three, the 30-person startup claims to have more than 300 education and corporate clients worldwide. “The Series A funding enables us to accelerate our goal of integrating novel voice technology into every communication workflow to solve name pronunciation across a wide spectrum of use cases during any voice interaction,” said Shanbhag, who wouldn’t Namecoach’s disclose revenue figures when asked. “Namecoach has customers across a very wide range of verticals, use cases and organization size, which means that we are not reliant on any subsegment to thrive in a downturn.” To date, Palo Alto-based Namecoach has raised $15 million in venture capital.

Bending Spoons acquires Evernote, marking the end of an era • ZebethMedia

By zebethcontrolPosted on November 16, 2022Posted in acquisition, App, Apps, Enterprise, Evernote, note taking, StartupsNo Comments

Evernote, the note-taking and task management app founded over 20 years ago, has been acquired by Milan-based app developer Bending Spoons. In a post on Evernote’s newsroom, Evernote CEO Ian Small said that Bending Spoons will take ownership of Evernote in a transaction expected to close in early 2023. “For Evernote, this decision is the next strategic step forward on our journey to be an extension of your brain,” Small wrote. “Teaming up with Bending Spoons will [accelerate] the delivery of improvements across our teams, professional, personal and free offerings.” For Evernote, the acquisition — the terms of which weren’t made public — marks the end of a roller coaster of a journey. Founded in 2000 by Russian-American entrepreneur Stepan Pachikov, Redwood City-based Evernote made handwriting recognition software for Windows and the eponymous note-taking, web-clipping app Evernote, which stored notes on an “infinite roll of paper.” Under CEO Phil Libin, who joined the company in 2007, Evernote shifted its focus to the web, smartphones and Mac, starting with Evernote 3.0 in 2008. This proved to be a winning strategy — at least at first. Between 2010 and 2015, Evernote raised hundreds of millions of dollars in venture capital from investors including Sequoia, Meritech Capital and Japanese media company Nikkei. Its web service reached 11 million users within the first three years and Evernote launched a business in China, Yinxiang Biji, as the startup sought to rapidly expand. In 2013, Evernote was reportedly valued at nearly a billion dollars. But then trouble set in. Evernote’s chief operating officer, appointed in June 2015, left after just a few months. Meanwhile, Libin pursued partnerships with physical goods brands like Moleskine and Pfeiffer, launching Evernote-branded desk accessory lines that failed to catch on in a major way. Former Google Glass executive Chris O’Neill replaced Libin in July 2015. And in October of that year, Evernote laid off 18% of its staff and closed three of its ten global offices. August 2018 saw an exodus of top execs, including Evernote’s chief technical officer, chief financial officer, chief product officer and head of HR. Fifteen percent of the company’s workforce was laid off in September 2018, a step O’Neill justified as necessary to correct for the company’s recent overexpansion and “inefficiency.” Small, the former CEO of platform-as-a-service company TokBox, came on in 2018. Under his leadership, Evernote hit $100 million in recurring revenue, millions of paying customers and over 250 million users. But it largely failed to keep pace with competitors like Notion, opting to rely heavily on a consumer-focused freemium model while eschewing the kinds of collaboration features embraced by its rivals. So what does Bending Spoons gain with the purchase? Another feather in its software cap, it’d seem. The European tech company makes apps like video editor Splice, 30 Day Fitness, Live Quiz and photo editor Remini, which combined have about 100 million users. Bending Spoons CEO Luca Ferrari says that Bending Spoons — fresh off of a $340 million venture round — will apply its “proprietary technologies” to Evernote to “augment its usefulness” and “strengthen its reach.” “Our mission at Bending Spoons is to make an enduring positive impact on our customers, on our teammates, and on society at large. Every day, millions of people across the globe rely on Evernote to organize their lives,” Ferrari said in a statement. “As such, Evernote is a perfect fit for the Bending Spoons portfolio, and we’re delighted to be able to serve its large and loyal customer base.”

Microsoft and Nvidia team up to build new Azure-hosted AI supercomputer • ZebethMedia

By zebethcontrolPosted on November 16, 2022Posted in AI, Enterprise, Microsoft, nvidia, Robotics & AI, supercomputerNo Comments

Roughly two years ago, Microsoft announced a partnership with OpenAI, the AI lab with which it has a close commercial relationship, to build what the tech giant called an “AI Supercomputer” running in the Azure cloud. Containing over 285,000 processor cores and 10,000 graphics cards, Microsoft claimed at the time that it was one of the largest supercomputer clusters in the world. Now, presumably to support even more ambitious AI workloads, Microsoft says it’s signed a “multi-year” deal with Nvidia to build a new supercomputer hosted in Azure and powered by Nvidia’s GPUs, networking and AI software for training AI systems. “AI is fueling the next wave of automation across enterprises and industrial computing, enabling organizations to do more with less as they navigate economic uncertainties,” Scott Guthrie, executive vice president of Microsoft’s cloud and AI group, said in a statement. “Our collaboration with Nvidia unlocks the world’s most scalable supercomputer platform, which delivers state-of-the-art AI capabilities for every enterprise on Microsoft Azure.” Details were hard to come by at press time. But in a blog post, Microsoft and Nvidia said that the upcoming supercomputer will feature hardware like Nvidia’s Quantum-2 400Gb/s InfiniBand networking technology and recently-detailed H100 GPUs. Current Azure instances offer previous-gen Nvidia A100 GPUs paired with Quantum 200Gb/s InfiniBand networking. Notably, the H100 — the flagship of Nvidia’s Hopper architecture — ships with a special “Transformer Engine” to accelerate machine learning tasks and — at least according to Nvidia — delivers between 1.5 and 6 times better performance than the A100. It’s also less power-hungry, offering the same performance as the A100 with up to 3.5 times better energy efficiency. One of the first industrial-scale machines to sport H100 GPUs, the Lenovo-built Henri system operated by the Flatiron Institute in New York City, topped the list of this year’s most efficient supercomputers. As part of the Microsoft collaboration, Nvidia says that it’ll use Azure virtual machine instances to research advances in generative AI, or the self-learning algorithms that can create text, code, images, video or audio. (Think along the lines of OpenAI’s text-generating GPT-3 and image-producing DALL-E 2.) Meanwhile, Microsoft will optimize its DeepSpeed library for new Nvidia hardware, aiming to reduce computing power and memory usage during AI training workloads, and work with Nvidia to make the company’s stack of AI workflows and software development kits available to Azure enterprise customers. Why Nvidia would opt to use Azure instances over its own in-house supercomputer, Selene, isn’t entirely clear; the company’s already tapped Selence to train generative AI like GauGAN2, a text-to-image generation model that creates art from basic sketches. Evidently, Nvidia anticipates that the scope of the AI systems that it’s working with will eventually surpass Selene’s capabilities. “AI technology advances as well as industry adoption are accelerating. The breakthrough of foundation models has triggered a tidal wave of research, fostered new startups and enabled new enterprise applications,” Manuvir Das, VP of enterprise computing at Nvidia, said in a statement. “Our collaboration with Microsoft will provide researchers and companies with state-of-the-art AI infrastructure and software to capitalize on the transformative power of AI.” The insatiable demand for powerful AI training infrastructure has led to an arms race of sorts among cloud and hardware vendors. Just this week, Cerabras, which has raised over $720 million in venture capital to date at an over-$4 billion valuation, unveiled a 13.5-million core AI supercomputer called Andromeda it claims can achieve more than 1 exaflop of AI compute. Google and Amazon continue to invest in their own proprietary solutions, offering custom-designed chips — e.g. TPUs and Trainium — for accelerating AI training in the cloud. The push for more powerful hardware will continue for the foreseeable future. A recent study found that the compute requirements for large-scale AI models has been doubling at an average rate of 10.7 months between 2016 and 2022. And OpenAI once estimated that, if GPT-3 were to be trained on a single Nvidia Tesla V100 GPU, it would take around 355 years.

Microsoft’s SQL Server 2022 is all about Azure • ZebethMedia

By zebethcontrolPosted on November 16, 2022Posted in cloud connection, database, developers, Enterprise, Microsoft, on-premises database, sql server, SQL Server 2022No Comments

Microsoft today released SQL Server 2022, the latest version of its database software, which originally launched more than 33 years ago. Microsoft describes this release as the “most Azure-enabled release of SQL Server yet” and with connections to Azure Synapse Link for enabling real-time analytics over the database, Azure Purview for data governance and disaster recovery with the help of Azure SQL Managed Instance, this release is, in many ways, the culmination of the cloud-connection groundwork the team started quite a few years ago. “From the very beginning, the vision [for SQL Server] really was about — databases were very complex — how do you make that extremely simple? And in many ways, I think that has been a key reason why it lasted for so long and how we’ve evolved it as well,” Ran Kumar, Microsoft’s corporate VP for Azure Data, told me. “One of the big things that I think about with SQL Server 2022 is that we’ve made it completely cloud-connected to Azure.” He noted that while the migration of on-prem workloads is happening, Microsoft’s customers are all moving at very different speeds and some, for a multitude of reasons, may never move to the cloud at all. That, he argues, is why the company always bet on a hybrid approach, but it is also why a lot of customers started asking about how they could get the value of being in the cloud without actually having to move all of their data to it. “That was really the key thesis of why we invested in making this into a cloud release,” Kumar said. Image Credits: Microsoft A good example here is the new disaster recovery function that allows users to replicate their data in SQL Managed Instance on Azure and use that as a backup for their main on-premises SQL Server, which should make it easy to fail over to that when the main server goes down. Kumar also noted that with Synapse Link, SQL Server users can now run real-time analytics over their database without having to set up a complex infrastructure. “All you need to do is check a box and say: ‘replicate this data in near real time.’ It lands it into Synapse and you can have your Power BI report that’s reading that data and that whole pipeline is just built for you,” he said. And for companies that do indeed have a hybrid setup, support for the Purview data governance service now enables them to set their policies, no matter whether the data resides in SQL Server in the cloud or on premises. In addition to the work on the new cloud-connected capabilities, the team also, of course, worked on improving the database’s overall performance, stability and security posture. At the core of that work, at least for this release, was the database’s intelligent query processing engine, which can now optimize queries in a number of more complex scenarios, for example. Also interesting is a new pay-as-you-go billing model for SQL server through Azure Arc, Microsoft’s platform for managing cloud and on-premises resources. Using a connection to Azure Arc, which is part of the SQL Server 2022 setup process, on-premises users can now also opt for cloud-enabled billing to manage consumption spikes or for ad hoc use cases. As Kumar noted, SQL Server usage, despite all of the competition available today, continues to grow (though in part, that’s driven by existing customers expanding their usage). The new edition of SQL Server is now generally available, including the free Developer and Express editions.