Jun
24
2020
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Cape Privacy launches data science collaboration platform with $5.06M seed investment

Cape Privacy emerged from stealth today after spending two years building a platform for data scientists to privately share encrypted data. The startup also announced $2.95 million in new funding and $2.11 million in funding it got when the business launched in 2018, for a total of $5.06 million raised.

Boldstart Ventures and Version One led the round, with participation from Haystack, Radical Ventures and Faktory Ventures.

Company CEO Ché Wijesinghe says that data science teams often have to deal with data sets that contain sensitive data and share data internally or externally for collaboration purposes. It creates a legal and regulatory data privacy conundrum that Cape Privacy is trying to solve.

“Cape Privacy is a collaboration platform designed to help focus on data privacy for data scientists. So the biggest challenge that people have today from a business perspective is managing privacy policies for machine learning and data science,” Wijesinghe told TechCrunch.

The product breaks down that problem into a couple of key areas. First of all it can take language from lawyers and compliance teams and convert that into code that automatically generates policies about who can see the different types of data in a given data set. What’s more, it has machine learning underpinnings so it also learns about company rules and preferences over time.

It also has a cryptographic privacy component. By wrapping the data with a cryptographic cypher, it lets teams share sensitive data in a safe way without exposing the data to people who shouldn’t be seeing it because of legal or regulatory compliance reasons.

“You can send something to a competitor as an example that’s encrypted, and they’re able to process that encrypted data without decrypting it, so they can train their model on encrypted data,” company co-founder and CTO Gavin Uhma explained.

The company closed the new round in April, which means they were raising in the middle of a pandemic, but it didn’t hurt that they had built the product already and were ready to go to market, and that Uhma and his co-founders had already built a successful startup, GoInstant, which was acquired by Salesforce in 2012. (It’s worth noting that GoInstant debuted at TechCrunch Disrupt in 2011.)

Uhma and his team brought Wijesinghe on board to build the sales and marketing team because, as a technical team, they wanted someone with go-to-market experience running the company so they could concentrate on building product.

The company has 14 employees and is already an all-remote team, so the team didn’t have to adjust at all when the pandemic hit. While it plans to keep hiring fairly limited for the foreseeable future, the company has had a diversity and inclusion plan from the start.

“You have to be intentional about about seeking diversity, so it’s something that when we sit down and map out our hiring and work with recruiters in terms of our pipeline, we really make sure that diversity is one of our objectives. You just have it as a goal, as part of your culture, and it’s something that when we see the picture of the team, we want to see diversity,” he said.

Wijesinghe adds, “As a person of color myself, I’m very sensitive to making sure that we have a very diverse team, not just from a color perspective, but a gender perspective as well.”

The company is gearing up to sell the product  and has paid pilots starting in the coming weeks.

Jun
22
2020
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ServiceNow to acquire Belgian configuration management startup Sweagle

With more companies moving workers home, making sure your systems are up and running has become more important than ever. ServiceNow, which includes in its product catalog an IT Help Desk component, recognizes that help desks have been bombarded during the pandemic. To help stop configuration problems before they start, the company today acquired Sweagle, a configuration management startup based in Belgium.

The companies did not share the purchase price.

ServiceNow gets a couple of boosts in the deal. First of all, it gets the startup’s configuration management products, which it can incorporate into its own catalog, but it also gains the machine learning and DevOps knowledge of the company’s employees. (The company would not share the exact number of employees, but PitchBook pegs it at 15.)

RJ Jainendra, ServiceNow’s vice president and general manager of DevOps and IT Business Management, sees a company that has pioneered the IT configuration management automation space, and brings with it capabilities that can boost ServiceNow’s offerings. “With capabilities for configuration data management from Sweagle, we will empower DevOps teams to deliver application and infrastructure changes more rapidly while reducing risk,” Jainendra said in a statement.

ServiceNow claims that there can be as many as 50,000 different configuration elements in a single enterprise application. Sweagle has designed a configuration data management platform with machine learning underpinnings to help customers simplify and automate that complexity. Configuration errors can cause shutdowns, security issues and other serious problems for companies.

Sweagle was founded in 2017 and raised $4.05 million on a post-valuation of $11.88 million, according to PitchBook data.

The company is part of a growing pattern of early-stage startups being sucked up by larger companies during the pandemic, including VMware acquiring Ocatarine and Atlassian buying Halp in May and NetApp snagging Spot earlier this month.

This is the third acquisition for ServiceNow this year, all involving AI underpinnings. In January it bought Loom Systems and Passsage AI. The deal is expected to close in Q3 this year, according to ServiceNow.

May
26
2020
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Scandit raises $80M as COVID-19 drives demand for contactless deliveries

Enterprise barcode scanner company Scandit has closed an $80 million Series C round, led by Silicon Valley VC firm G2VP. Atomico, GV, Kreos, NGP Capital, Salesforce Ventures and Swisscom Ventures also participated in the round — which brings its total raised to date to $123M.

The Zurich-based firm offers a platform that combines computer vision and machine learning tech with barcode scanning, text recognition (OCR), object recognition and augmented reality which is designed for any camera-equipped smart device — from smartphones to drones, wearables (e.g. AR glasses for warehouse workers) and even robots.

Use-cases include mobile apps or websites for mobile shopping; self checkout; inventory management; proof of delivery; asset tracking and maintenance — including in healthcare where its tech can be used to power the scanning of patient IDs, samples, medication and supplies.

It bills its software as “unmatched” in terms of speed and accuracy, as well as the ability to scan in bad light; at any angle; and with damaged labels. Target industries include retail, healthcare, industrial/manufacturing, travel, transport & logistics and more.

The latest funding injection follows a $30M Series B round back in 2018. Since then Scandit says it’s tripled recurring revenues, more than doubling the number of blue-chip enterprise customers, and doubling the size of its global team.

Global customers for its tech include the likes of 7-Eleven, Alaska Airlines, Carrefour, DPD, FedEx, Instacart, Johns Hopkins Hospital, La Poste, Levi Strauss & Co, Mount Sinai Hospital and Toyota — with the company touting “tens of billions of scans” per year on 100+ million active devices at this stage of its business.

It says the new funding will go on further pressing on the gas to grow in new markets, including APAC and Latin America, as well as building out its footprint and ops in North America and Europe. Also on the slate: Funding more R&D to devise new ways for enterprises to transform their core business processes using computer vision and AR.

The need for social distancing during the coronavirus pandemic has also accelerated demand for mobile computer vision on personal smart devices, according to Scandit, which says customers are looking for ways to enable more contactless interactions.

Another demand spike it’s seeing is coming from the pandemic-related boom in ‘Click & Collect’ retail and “millions” of extra home deliveries — something its tech is well positioned to cater to because its scanning apps support BYOD (bring your own device), rather than requiring proprietary hardware.

“COVID-19 has shone a spotlight on the need for rapid digital transformation in these uncertain times, and the need to blend the physical and digital plays a crucial role,” said CEO Samuel Mueller in a statement. “Our new funding makes it possible for us to help even more enterprises to quickly adapt to the new demand for ‘contactless business’, and be better positioned to succeed, whatever the new normal is.”

Also commenting on the funding in a supporting statement, Ben Kortlang, general partner at G2VP, added: “Scandit’s platform puts an enterprise-grade scanning solution in the pocket of every employee and customer without requiring legacy hardware. This bridge between the physical and digital worlds will be increasingly critical as the world accelerates its shift to online purchasing and delivery, distributed supply chains and cashierless retail.”

May
19
2020
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Microsoft launches Project Bonsai, its new machine teaching service for building autonomous systems

At its Build developer conference, Microsoft today announced that Project Bonsai, its new machine teaching service, is now in public preview.

If that name sounds familiar, it’s probably because you remember that Microsoft acquired Bonsai, a company that focuses on machine teaching, back in 2018. Bonsai combined simulation tools with different machine learning techniques to build a general-purpose deep reinforcement learning platform, with a focus on industrial control systems.

It’s maybe no surprise then that Project Bonsai, too, has a similar focus on helping businesses teach and manage their autonomous machines. “With Project Bonsai, subject-matter experts can add state-of-the-art intelligence to their most dynamic physical systems and processes without needing a background in AI,” the company notes in its press materials.

“The public preview of Project Bonsai builds on top of the Bonsai acquisition and the autonomous systems private preview announcements made at Build and Ignite of last year,” a Microsoft spokesperson told me.

Interestingly, Microsoft notes that project Bonsai is only the first block of a larger vision to help its customers build these autonomous systems. The company also stresses the advantages of machine teaching over other machine learning approach, especially the fact that it’s less of a black box approach than other methods, which makes it easier for developers and engineers to debug systems that don’t work as expected.

In addition to Bonsai, Microsoft also today announced Project Moab, an open-source balancing robot that is meant to help engineers and developers learn the basics of how to build a real-world control system. The idea here is to teach the robot to keep a ball balanced on top of a platform that is held by three arms.

Potential users will be able to either 3D print the robot themselves or buy one when it goes on sale later this year. There is also a simulation, developed by MathWorks, that developers can try out immediately.

“You can very quickly take it into areas where doing it in traditional ways would not be easy, such as balancing an egg instead,” said Mark Hammond, Microsoft General Manager
for Autonomous Systems. “The point of the Project Moab system is to provide that
playground where engineers tackling various problems can learn how to use the tooling and simulation models. Once they understand the concepts, they can apply it to their novel use case.”

May
14
2020
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Adobe announces AI toolbox for Experience Platform

Most companies don’t have the personnel to do AI well, so they turn to platform vendors like Adobe for help. Like other platforms, it has been building AI into its product set for several years now, but wanted to give marketers a set of tools that take advantage of some advanced AI capabilities out of the box.

Today, the company announced five pre-packaged AI solutions specifically designed to give marketers more intelligent insight. Amit Ahuja, VP of ecosystem development at Adobe, says even before the pandemic, customers were struggling to deal with the onslaught of data and how they could use it to understand their customers better.

“There is so much data coming in, and customers are struggling to leverage this data — and not just for the purpose of analytics and insights, which is a huge part of it, but also to do predictive optimization,” Ahuja explained.

What’s more, we’ve known for some time that when there is so much data, it becomes impossible to make sense of it manually. Given that AI deals best with tons of data, Adobe wanted to take advantage of that, while packaging some popular data scenarios in a way that makes it easy for marketers to get insights.

That data comes from the Adobe Experience Platform, which the is designed to pull data not only from Adobe products, but from a variety of enterprise sources to help marketers build a more complete picture of their customers and get answers to key questions.

Customer Insights AI helps users understand their customers better. Image Credit: Adobe

The company is announcing a total of five AI tools today, two of which are generally available with the remainder in Beta for now. For starters, Customer AI helps marketers understand why their customers do what they do. For instance, why they keep coming back or why they stopped. Attribution AI helps marketers understand how effective their strategies are, something that’s always important, but especially in this economy where effectively deploying spend is more important than ever.

The first of the Beta tools is Journey AI, which helps marketers decide the best channel to engage customers. Content and Commerce AI looks at the most effective way to deliver content and finally Leads AI looks at the visitors most likely to convert to customers.

These five are just a start, and the company plans to add new tools to the toolbox as customers look for additional insights from the data to help them improve their marketing outcomes.

May
12
2020
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SiMa.ai announces $30M Series A to build out lower-power edge chip solution

Krishna Rangasayee, founder and CEO, at SiMa.ai, has 30 years of experience in the semiconductor industry. He decided to put that experience to work in a startup and launched SiMa.ai last year with the goal of building an ultra low-power software and chip solution for machine learning at the edge.

Today he announced a $30 million Series A led by Dell Technologies Capital with help from Amplify Partners, Wing Venture Capital and +ND Capital. Today’s investment brings the total raised to $40 million, according to the company.

Rangasayee says in his years as a chip executive he saw a gap in the machine learning market for embedded devices running at the edge and he decided to start the company to solve that issue.

“While the majority of the market was serviced by traditional computing, machine learning was beginning to make an impact and it was really amazing. I wanted to build a company that would bring machine learning at significant scale to help the problems with embedded markets,” he told TechCrunch.

The company is trying to focus on efficiency, which it says will make the solution more environmentally friendly by using less power. “Our solution can scale high performance at the lowest power efficiency, and that translates to the highest frames per second per watt. We have built out an architecture and a software solution that is at a minimum 30x better than anybody else on the frames per second,” he explained.

He added that achieving that efficiency required them to build a chip from scratch because there isn’t a solution available off the shelf today that could achieve that.

So far the company has attracted 20 early design partners, who are testing what they’ve built. He hopes to have the chip designed and the software solution in Beta in the Q4 timeframe this year, and is shooting for chip production by Q2 in 2021.

He recognizes that it’s hard to raise this kind of money in the current environment and he’s grateful to the investors, and the design partners who believe in his vision. The timing could actually work in the company’s favor because it can hunker down and build product while navigating through the current economic malaise.

Perhaps by 2021 when the product is in production, the market and the economy will be in better shape and the company will be ready to deliver.

May
11
2020
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Amazon releases Kendra to solve enterprise search with AI and machine learning

Enterprise search has always been a tough nut to crack. The Holy Grail has always been to operate like Google, but in-house. You enter a few keywords and you get back that nearly perfect response at the top of the list of the results. The irony of trying to do search locally has been a lack of content.

While Google has the universe of the World Wide Web to work with, enterprises have a much narrower set of responses. It would be easy to think that should make it easier to find the ideal response, but the fact is that it’s the opposite. The more data you have, the more likely you’ll find the correct document.

Amazon is trying to change the enterprise search game by putting it into a more modern machine learning-driven context to use today’s technology to help you find that perfect response just as you typically do on the web.

Today the company announced the general availability of Amazon Kendra, its cloud enterprise search product that the company announced last year at AWS re:Invent. It uses natural language processing to allow the user to simply ask a question, then searches across the repositories connected to the search engine to find a precise answer.

“Amazon Kendra reinvents enterprise search by allowing end-users to search across multiple silos of data using real questions (not just keywords) and leverages machine learning models under the hood to understand the content of documents and the relationships between them to deliver the precise answers they seek (instead of a random list of links),” the company described the new service in a statement.

AWS has tuned the search engine for specific industries including IT, healthcare and insurance. It promises energy, industrial, financial services, legal, media and entertainment, travel and hospitality, human resources, news, telecommunications, mining, food and beverage and automotive will be coming later this year.

This means any company in one of those industries should have a head start when it comes to searching because the system will understand the language specific to those verticals. You can drop your Kendra search box into an application or a website, and it has features like type ahead you would expect in a tool like this.

Enterprise search has been around for a long time, but perhaps by bringing AI and machine learning to bear on it, we can finally solve it once and for all.

May
07
2020
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Dtex, a specialist in insider threat cybersecurity, raises $17.5M

A lot of enterprise cybersecurity efforts focus on malicious hackers that work on behalf of larger organizations, be they criminal groups or state actors — and for good reason, since the majority of incidents these days come from phishing and other malicious techniques that originate outside the enterprise itself.

But there has also been a persistent, and now growing, focus also on “insider threats” — that is, breaches that start from within organizations themselves. And today a startup that specialises in this area is announcing a round of growth funding to expand its reach.

Dtex, which uses machine learning to monitor network activity within the perimeter and around all endpoints to detect unusual patterns or behaviour around passwords, data movement and other network activities, is today announcing that it has raised $17.5 million in funding.

The round is being led by new investor Northgate Capital with Norwest Venture Partners and Four Rivers Group, both previous investors, also participating. Prior to this, the San Jose-based startup had raised $57.5 million, according to data from PitchBook, while CrunchBase puts the total raised at $40 million.

CEO Bahman Mahbod said the startup is not disclosing valuation except to say that it’s “very excited” about it.

For some context, the company works with hundreds of large enterprises, primarily in the financial, critical infrastructure, government and defence sectors. The plan is to now extend further into newer verticals where it’s started to see more activity more recently: pharmaceuticals, life sciences and manufacturing. Dtex says that over the past 12 months, 80% of its top customers have been increasing their level of engagement with the startup.

Dtex’s focus on “insider” threats sounds slightly sinister at first. Is the implication here that people are more dishonest and nefarious these days and thus need to be policed and monitored much more closely for wrongdoing? The answer is no. There are no more dishonest people today than there ever have been, but there are a lot more opportunities to make mistakes that result in security breaches.

The working world has been on a long-term trend of becoming increasingly digitised in all of its interactions, and bringing on a lot more devices onto those networks. Across both “knowledge” and front-line workers, we now have a vastly larger number of devices being used to help workers do their jobs or just keep in touch with the company as they work, with many of them being brought by the workers themselves rather than being provisioned by the companies. There has also been a huge increase in cloud services,

And in the realm of “knowledge” workers, we’re seeing a lot more remote or peripatetic working, where people don’t have fixed desks and often work outside the office altogether — something that has skyrocketed in recent times with stay-at-home orders put in place to mitigate the spread of COVID-19 cases.

All of this translates into a much wider threat “horizon” within organizations themselves, before even considering the sophistication of external malicious hackers.

And the current state of business has exacerbated that. Mahbod tells us that Dtex is currently seeing spikes in unusual activity from the rise in home workers, who sometimes circumvent VPNs and other security controls, thus committing policy violations; as well as more problems arising from the fact that home networks have been compromised and that is leaving work networks, accessed from home, more vulnerable. These started, he said, with COVID-19 phishing attacks but have progressed to undetected malware from drive-by downloads.

And, inevitably, he added that there has been a rise in intentional data theft and accidental loss arising in cases where organizations have had to lay people off or run a round of furloughs, but might still result from negligence rather than intentional actions.

There are a number of other cybersecurity companies that provide ways to detect insider threats — they include CloudKnox and Obsidian Security, along with a number of larger and established vendors. But Mabhod says that Dtex “is the only company with ‘next-generation’ capabilities that are cloud-first, AI/ML baked-in, and enterprise scalable to millions of users and devices, which it sells as DMAP+.

“Effectively, Next-Gen Insider Threat solutions must replace legacy Insider Threat point solutions which were borne out of the UAM, DLP and UEBA spaces,” he said.

Those providing legacy approaches of that kind include Forcepoint with its SureView product and Proofpoint with its ObserveIT product. Interestingly, CyberX, which is currently in the process of getting acquired by Microsoft (according to reports and also our sources), also includes insider threats in its services.

This is one reason why investors have been interested.

“Dtex has built a highly scalable platform that utilizes a cloud-first, lightweight endpoint architecture, offering clients a number of use cases including insider threat prevention and business operations intelligence,” said Thorsten Claus, partner, Northgate Capital, in a statement. Northgate has a long list of enterprise startups in its portfolio that represent potential customers but also a track record of experience in assessing the problem at hand and building products to address it. “With Dtex, we have found a fast-growing, long-term, investible operation that is not just a band-aid collection of tools, which would be short-lived and replaced.”

May
06
2020
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Enterprise companies find MLOps critical for reliability and performance

Enterprise startups UIPath and Scale have drawn huge attention in recent years from companies looking to automate workflows, from RPA (robotic process automation) to data labeling.

What’s been overlooked in the wake of such workflow-specific tools has been the base class of products that enterprises are using to build the core of their machine learning (ML) workflows, and the shift in focus toward automating the deployment and governance aspects of the ML workflow.

That’s where MLOps comes in, and its popularity has been fueled by the rise of core ML workflow platforms such as Boston-based DataRobot. The company has raised more than $430 million and reached a $1 billion valuation this past fall serving this very need for enterprise customers. DataRobot’s vision has been simple: enabling a range of users within enterprises, from business and IT users to data scientists, to gather data and build, test and deploy ML models quickly.

Founded in 2012, the company has quietly amassed a customer base that boasts more than a third of the Fortune 50, with triple-digit yearly growth since 2015. DataRobot’s top four industries include finance, retail, healthcare and insurance; its customers have deployed over 1.7 billion models through DataRobot’s platform. The company is not alone, with competitors like H20.ai, which raised a $72.5 million Series D led by Goldman Sachs last August, offering a similar platform.

Why the excitement? As artificial intelligence pushed into the enterprise, the first step was to go from data to a working ML model, which started with data scientists doing this manually, but today is increasingly automated and has become known as “auto ML.” An auto-ML platform like DataRobot’s can let an enterprise user quickly auto-select features based on their data and auto-generate a number of models to see which ones work best.

As auto ML became more popular, improving the deployment phase of the ML workflow has become critical for reliability and performance — and so enters MLOps. It’s quite similar to the way that DevOps has improved the deployment of source code for applications. Companies such as DataRobot and H20.ai, along with other startups and the major cloud providers, are intensifying their efforts on providing MLOps solutions for customers.

We sat down with DataRobot’s team to understand how their platform has been helping enterprises build auto-ML workflows, what MLOps is all about and what’s been driving customers to adopt MLOps practices now.

The rise of MLOps

May
06
2020
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Run:AI brings virtualization to GPUs running Kubernetes workloads

In the early 2000s, VMware introduced the world to virtual servers that allowed IT to make more efficient use of idle server capacity. Today, Run:AI is introducing that same concept to GPUs running containerized machine learning projects on Kubernetes.

This should enable data science teams to have access to more resources than they would normally get were they simply allocated a certain number of available GPUs. Company CEO and co-founder Omri Geller says his company believes that part of the issue in getting AI projects to market is due to static resource allocation holding back data science teams.

“There are many times when those important and expensive computer sources are sitting idle, while at the same time, other users that might need more compute power since they need to run more experiments and don’t have access to available resources because they are part of a static assignment,” Geller explained.

To solve that issue of static resource allocation, Run:AI came up with a solution to virtualize those GPU resources, whether on prem or in the cloud, and let IT define by policy how those resources should be divided.

“There is a need for a specific virtualization approaches for AI and actively managed orchestration and scheduling of those GPU resources, while providing the visibility and control over those compute resources to IT organizations and AI administrators,” he said.

Run:AI creates a resource pool, which allocates based on need. Image Credits Run:AI

Run:AI built a solution to bridge this gap between the resources IT is providing to data science teams and what they require to run a given job, while still giving IT some control over defining how that works.

“We really help companies get much more out of their infrastructure, and we do it by really abstracting the hardware from the data science, meaning you can simply run your experiment without thinking about the underlying hardware, and at any moment in time you can consume as much compute power as you need,” he said.

While the company is still in its early stages, and the current economic situation is hitting everyone hard, Geller sees a place for a solution like Run:AI because it gives customers the capacity to make the most out of existing resources, while making data science teams run more efficiently.

He also is taking a realistic long view when it comes to customer acquisition during this time. “These are challenging times for everyone,” he says. “We have plans for longer time partnerships with our customers that are not optimized for short term revenues.”

Run:AI was founded in 2018. It has raised $13 million, according to Geller. The company is based in Israel with offices in the United States. It currently has 25 employees and a few dozen customers.

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