Jul
18
2018
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Swim.ai raises $10M to bring real-time analytics to the edge

Once upon a time, it looked like cloud-based serviced would become the central hub for analyzing all IoT data. But it didn’t quite turn out that way because most IoT solutions simply generate too much data to do this effectively and the round-trip to the data center doesn’t work for applications that have to react in real time. Hence the advent of edge computing, which is spawning its own ecosystem of startups.

Among those is Swim.ai, which today announced that it has raised a $10 million Series B funding round led by Cambridge Innovation Capital, with participation from Silver Creek Ventures and Harris Barton Asset Management. The round also included a strategic investment from Arm, the chip design firm you may still remember as ARM (but don’t write it like that or their PR department will promptly email you). This brings the company’s total funding to about $18 million.

Swim.ai has an interesting take on edge computing. The company’s SWIM EDX product combines both local data processing and analytics with local machine learning. In a traditional approach, the edge devices collect the data, maybe perform some basic operations against the data to bring down the bandwidth cost and then ship it to the cloud where the hard work is done and where, if you are doing machine learning, the models are trained. Swim.ai argues that this doesn’t work for applications that need to respond in real time. Swim.ai, however, performs the model training on the edge device itself by pulling in data from all connected devices. It then builds a digital twin for each one of these devices and uses that to self-train its models based on this data.

“Demand for the EDX software is rapidly increasing, driven by our software’s unique ability to analyze and reduce data, share new insights instantly peer-to-peer – locally at the ‘edge’ on existing equipment. Efficiently processing edge data and enabling insights to be easily created and delivered with the lowest latency are critical needs for any organization,” said Rusty Cumpston, co-founder and CEO of Swim.ai. “We are thrilled to partner with our new and existing investors who share our vision and look forward to shaping the future of real-time analytics at the edge.”

The company doesn’t disclose any current customers, but it is focusing its efforts on manufacturers, service providers and smart city solutions. Update: Swim.ai did tell us about two customers after we published this story: The City of Palo Alto and Itron.

Swim.ai plans to use its new funding to launch a new R&D center in Cambridge, UK, expand its product development team and tackle new verticals and geographies with an expanded sales and marketing team.

Jul
12
2018
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Datadog launches Watchdog to help you monitor your cloud apps

Your typical cloud monitoring service integrates with dozens of service and provides you a pretty dashboard and some automation to help you keep tabs on how your applications are doing. Datadog has long done that but today, it is adding a new service called Watchdog, which uses machine learning to automatically detect anomalies for you.

The company notes that a traditional monitoring setup involves defining your parameters based on how you expect the application to behave and then set up dashboards and alerts to monitor them. Given the complexity of modern cloud applications, that approach has its limits, so an additional layer of automation becomes necessary.

That’s where Watchdog comes in. The service observes all of the performance data it can get its paws on, learns what’s normal, and then provides alerts when something unusual happens and — ideally — provides insights into where exactly the issue started.

“Watchdog builds upon our years of research and training of algorithms on our customers data sets. This technology is unique in that it not only identifies an issue programmatically, but also points users to probable root causes to kick off an investigation,” Datadog’s head of data science Homin Lee notes in today’s announcement.

The service is now available to all Datadog customers in its Enterprise APM plan.

Jun
28
2018
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Facebook is using machine learning to self-tune its myriad services

Regardless of what you may think of Facebook as a platform, they run a massive operation, and when you reach their level of scale you have to get more creative in how you handle every aspect of your computing environment.

Engineers quickly reach the limits of human ability to track information, to the point that checking logs and analytics becomes impractical and unwieldy on a system running thousands of services. This is a perfect scenario to implement machine learning, and that is precisely what Facebook has done.

The company published a blog post today about a self-tuning system they have dubbed Spiral. This is pretty nifty, and what it does is essentially flip the idea of system tuning on its head. Instead of looking at some data and coding what you want the system to do, you teach the system the right way to do it and it does it for you, using the massive stream of data to continually teach the machine learning models how to push the systems to be ever better.

In the blog post, the Spiral team described it this way: “Instead of looking at charts and logs produced by the system to verify correct and efficient operation, engineers now express what it means for a system to operate correctly and efficiently in code. Today, rather than specify how to compute correct responses to requests, our engineers encode the means of providing feedback to a self-tuning system.”

They say that coding in this way is akin to declarative code, like using SQL statements to tell the database what you want it to do with the data, but the act of applying that concept to systems is not a simple matter.

“Spiral uses machine learning to create data-driven and reactive heuristics for resource-constrained real-time services. The system allows for much faster development and hands-free maintenance of those services, compared with the hand-coded alternative,” the Spiral team wrote in the blog post.

If you consider the sheer number of services running on Facebook, and the number of users trying to interact with those services at any given time, it required sophisticated automation, and that is what Spiral is providing.

The system takes the log data and processes it through Spiral, which is connected with just a few lines of code. It then sends commands back to the server based on the declarative coding statements written by the team. To ensure those commands are always being fine-tuned, at the same time, the data gets sent from the server to a model for further adjustment in a lovely virtuous cycle. This process can be applied locally or globally.

The tool was developed by the team operating in Boston, and is only available internally inside Facebook. It took lots of engineering to make it happen, the kind of scope that only Facebook could apply to a problem like this (mostly because Facebook is one of the few companies that would actually have a problem like this).

Jun
11
2018
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Splunk nabs on-call management startup VictorOps for $120M

In a DevOps world, the operations part of the equation needs to be on call to deal with issues as they come up 24/7. We used to use pagers. Today’s solutions like PagerDuty and VictorOps have been created to place this kind of requirement in a modern digital context. Today, Splunk bought VictorOps for $120 million in cash and Splunk securities.

It’s a company that makes a lot of sense for Splunk, a log management tool that has been helping customers deal with oodles of information being generated from back-end systems for many years. With VictorOps, the company gets a system to alert the operations team when something from that muddle of data actually requires their attention.

Splunk has been making moves in recent years to use artificial intelligence and machine learning to help make sense of the data and provide a level of automation required when the sheer volume of data makes it next to impossible for humans to keep up. VictorOps fits within that approach.

“The combination of machine data analytics and artificial intelligence from Splunk with incident management from VictorOps creates a ‘Platform of Engagement’ that will help modern development teams innovate faster and deliver better customer experiences,” Doug Merritt, president and CEO at Splunk said in a statement.

In a blog post announcing the deal, VictorOps founder and CEO Todd Vernon said the two companies’ missions are aligned. “Upon close, VictorOps will join Splunk’s IT Markets group and together will provide on-call technical staff an analytics and AI-driven approach for addressing the incident lifecycle, from monitoring to response to incident management to continuous learning and improvement,” Vernon wrote.

It should come as no surprise that the two companies have been working together even before the acquisition. “Splunk has been an important technical partner of ours for some time, and through our work together, we discovered that we share a common viewpoint that Modern Incident Management is in a period of strategic change where data is king, and insights from that data are key to maintaining a market leading strategy,” Vernon wrote in the blog post.

VictorOps was founded 2012 and has raised over $33 million, according to data on Crunchbase. The most recent investment was a $15 million Series B in December 2016.

The deal is expected to close in Splunk’s fiscal second quarter subject to customary closing conditions, according to a statement from Splunk.

Jun
11
2018
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Workday acquires financial modelling startup Adaptive Insights for $1.55B

Workday, the cloud-based platform that offers HR and other back-office apps for businesses, is making an acquisition to expand its portfolio of services: It’s buying Adaptive Insights, a provider of cloud-based business planning and financial modelling tools, for $1.55 billion. The acquisition is notable because Adaptive Insights had filed for an IPO as recently as May 17.

Workday says that the $1.55 billion price tag includes “the assumption of approximately $150 million in unvested equity issued to Adaptive Insights employees” related to that IPO. This deal is expected to close in Q3 of this year.

IPO filings are known to sometimes trigger M&A. Most recently, PayPal announced it would acquire iZettle just after the latter filed to go public. Skype was acquired by Microsoft in 2011 while it was waiting to IPO after previous owner eBay said it would spin it off.

Workday itself went public in 2012 and currently has a market cap of nearly $27 billion.

The deal will give Workday another string to its bow, in its attempt to become the go-to place for all for back-office services for its business customers: the company plans to integrate Adaptive Insights’ tools into its existing platform.

“Adaptive Insights is an industry leader with its Business Planning Cloud platform, and together with Workday, we will help customers accelerate their finance transformation in the cloud,” said Aneel Bhusri, Co-Founder and CEO, Workday, in a statement. “I am excited to welcome the Adaptive Insights team to Workday and look forward to coming together to continue delivering industry-leading products that equip finance organizations to make even faster, better business decisions to adapt to change and to drive growth.”

The two have been working together as partners since 2015.

In the case of Adaptive Insights, which says it has ‘thousands’ of customers, its growth mirrors that both of cloud services and specifically about how business intelligence has developed into a distinct software category of its own over the years, with not just the CFO but an army of in-house analysts relying on analytics of a business’ data to help make small and big decisions.

“The market opportunity here is huge as the CFO has become a power player in the C-Suite,” CEO Tom Bogan told TechCrunch when it raised $75 million in 2015, when it first passed the billion-dollar mark for its valuation. Bogan previously also held a role as chairman of Citrix. “As a former CFO myself, I have seen this first hand and it is accelerating.” Other examples of this force includes Twitter’s Anthony Noto catapulting from CFO to COO (and is now a CEO running SoFi). Around 25 percent of CEOs at Fortune 500 companies are former CFOs.

Adaptive Insights had raised $175 million prior to this.

Bogan will stay on and lead the business and report directly to Bhusri.

“Joining forces with Workday accelerates our vision to drive holistic business planning and digital transformation for our customers,” said Bogan, in a separate statement. “Most importantly, both Adaptive Insights and Workday have an employee-first and customer-centric approach to developing enterprise software that will only increase the power of the combined companies.”

More generally, while we have certainly seen a much wider opening of the door for tech IPOs this year, there is also an argument to be made for continuing consolidation it enterprise IT, in particular with regards to cloud services that might have small or potentially negative margins.

Adaptive Insights was not immune to that: the company in its public listing filing said that its previous fiscal year brough tin $106.5 million in revenues, up 30 percent from the year before, but it also posted a loss of $42.7 million in the same period. That was narrower than the $59.1 million it posted in 2016. Combined with the bigger trend of all-in-one platforms packing a bigger punch with businesses, it might have meant that Workday’s offer was too compelling to refuse. 

This looks like Workday’s biggest acquisition yet, but the company has been on a spree of sorts: just last week it announced the acquisition of RallyTeam to beef up its machine learning.

Jun
08
2018
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Workday acquires Rallyteam to fuel machine learning efforts

Sometimes you acquire a company for the assets and sometimes you do it for the talent. Today Workday announced it was buying Rallyteam, a San Francisco startup that helps companies keep talented employees by matching them with more challenging opportunities in-house.

The companies did not share the purchase price or the number of Rallyteam employees who would be joining Workday .

In this case, Workday appears to be acquiring the talent. It wants to take the Rallyteam team and incorporate it into the company’s engineering unit to beef up its machine learning efforts, while taking advantage of the expertise it has built up over the years connecting employees with interesting internal projects.

“With Rallyteam, we gain incredible team members who created a talent mobility platform that uses machine learning to help companies better understand and optimize their workforces by matching a worker’s interests, skills and connections with relevant jobs, projects, tasks and people,” Workday’s Cristina Goldt wrote in a blog post announcing the acquisition.

Rallyteam, which was founded in 2013, and launched at TechCrunch Disrupt San Francisco in September 2014, helps employees find interesting internal projects that might otherwise get outsourced. “I knew there were opportunities that existed [internally] because as a manager, I was constantly outsourcing projects even though I knew there had to be people in the company that could solve this problem,” Rallyteam’s Huan Ho told TechCrunch’s Frederic Lardinois at the launch. Rallyteam was a service designed to solve this issue.

Last fall the company raised $8.6 million led by Norwest Ventures with participation from Storm Ventures, Cornerstone OnDemand and Wilson Sonsini.

Workday provides a SaaS platform for human resources and finance, so the Rallyteam approach fits nicely within the scope of the Workday business. This is the 10th acquisition for Workday and the second this year.

Chart: Crunchbase

Workday raised over $230 million before going public in 2012.

May
07
2018
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Microsoft and DJI team up to bring smarter drones to the enterprise

At the Microsoft Build developer conference today, Microsoft and Chinese drone manufacturer DJI announced a new partnership that aims to bring more of Microsoft’s machine learning smarts to commercial drones. Given Microsoft’s current focus on bringing intelligence to the edge, this is almost a logical partnership, given that drones are essentially semi-autonomous edge computing devices.

DJI also today announced that Azure is now its preferred cloud computing partner and that it will use the platform to analyze video data, for example. The two companies also plan to offer new commercial drone solutions using Azure IoT Edge and related AI technologies for verticals like agriculture, construction and public safety. Indeed, the companies are already working together on Microsoft’s FarmBeats solution, an AI and IoT platform for farmers.

As part of this partnership, DJI is launching a software development kit (SDK) for Windows that will allow Windows developers to build native apps to control DJI drones. Using the SDK, developers can also integrate third-party tools for managing payloads or accessing sensors and robotics components on their drones. DJI already offers a Windows-based ground station.

“DJI is excited to form this unique partnership with Microsoft to bring the power of DJI aerial platforms to the Microsoft developer ecosystem,” said Roger Luo, DJI president, in today’s announcement. “Using our new SDK, Windows developers will soon be able to employ drones, AI and machine learning technologies to create intelligent flying robots that will save businesses time and money and help make drone technology a mainstay in the workplace.”

Interestingly, Microsoft also stresses that this partnership gives DJI access to its Azure IP Advantage program. “For Microsoft, the partnership is an example of the important role IP plays in ensuring a healthy and vibrant technology ecosystem and builds upon existing partnerships in emerging sectors such as connected cars and personal wearables,” the company notes in today’s announcement.

May
07
2018
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Microsoft brings more AI smarts to the edge

At its Build developer conference this week, Microsoft is putting a lot of emphasis on artificial intelligence and edge computing. To a large degree, that means bringing many of the existing Azure services to machines that sit at the edge, no matter whether that’s a large industrial machine in a warehouse or a remote oil-drilling platform. The service that brings all of this together is Azure IoT Edge, which is getting quite a few updates today. IoT Edge is a collection of tools that brings AI, Azure services and custom apps to IoT devices.

As Microsoft announced today, Azure IoT Edge, which sits on top of Microsoft’s IoT Hub service, is now getting support for Microsoft’s Cognitive Services APIs, for example, as well as support for Event Grid and Kubernetes containers. In addition, Microsoft is also open sourcing the Azure IoT Edge runtime, which will allow developers to customize their edge deployments as needed.

The highlight here is support for Cognitive Services for edge deployments. Right now, this is a bit of a limited service as it actually only supports the Custom Vision service, but over time, the company plans to bring other Cognitive Services to the edge as well. The appeal of this service is pretty obvious, too, as it will allow industrial equipment or even drones to use these machine learning models without internet connectivity so they can take action even when they are offline.

As far as AI goes, Microsoft also today announced that it will bring its new Brainwave deep neural network acceleration platform for real-time AI to the edge.

The company has also teamed up with Qualcomm to launch an AI developer kit for on-device inferencing on the edge. The focus of the first version of this kit will be on camera-based solutions, which doesn’t come as a major surprise given that Qualcomm recently launched its own vision intelligence platform.

IoT Edge is also getting a number of other updates that don’t directly involve machine learning. Kubernetes support is an obvious one and a smart addition, given that it will allow developers to build Kubernetes clusters that can span both the edge and a more centralized cloud.

The appeal of running Event Grid, Microsoft’s event routing service, at the edge is also pretty obvious, given that it’ll allow developers to connect services with far lower latency than if all the data had to run through a remote data center.

Other IoT Edge updates include the planned launch of a marketplace that will allow Microsoft partners and developers to share and monetize their edge modules, as well as a new certification program for hardware manufacturers to ensure that their devices are compatible with Microsoft’s platform. IoT Edge, as well as Windows 10 IoT and Azure Machine Learning, will also soon support hardware-accelerated model evaluation with DirextX 12 GPU, which is available in virtually every modern Windows PC.

May
04
2018
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Google Kubeflow, machine learning for Kubernetes, begins to take shape

Ever since Google created Kubernetes as an open source container orchestration tool, it has seen it blossom in ways it might never have imagined. As the project gains in popularity, we are seeing many adjunct programs develop. Today, Google announced the release of version 0.1 of the Kubeflow open source tool, which is designed to bring machine learning to Kubernetes containers.

While Google has long since moved Kubernetes into the Cloud Native Computing Foundation, it continues to be actively involved, and Kubeflow is one manifestation of that. The project was only first announced at the end of last year at Kubecon in Austin, but it is beginning to gain some momentum.

David Aronchick, who runs Kubeflow for Google, led the Kubernetes team for 2.5 years before moving to Kubeflow. He says the idea behind the project is to enable data scientists to take advantage of running machine learning jobs on Kubernetes clusters. Kubeflow lets machine learning teams take existing jobs and simply attach them to a cluster without a lot of adapting.

With today’s announcement, the project begins to move ahead, and according to a blog post announcing the milestone, brings a new level of stability, while adding a slew of new features that the community has been requesting. These include Jupyter Hub for collaborative and interactive training on machine learning jobs and Tensorflow training and hosting support, among other elements.

Aronchick emphasizes that as an open source project you can bring whatever tools you like, and you are not limited to Tensorflow, despite the fact that this early version release does include support for Google’s machine learning tools. You can expect additional tool support as the project develops further.

In just over 4 months since the original announcement, the community has grown quickly with over 70 contributors, over 20 contributing organizations along with over 700 commits in 15 repositories. You can expect the next version, 0.2, sometime this summer.

Apr
09
2018
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Juro grabs $2M to take the hassle out of contracts

UK startup Juro, which is applying a “design centric approach” and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital.

Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise’s co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com).

Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round — with Point Nine Capital only joining “late” in the day.

“We actually could have strung it out to Series A,” he says of the funding that’s being announced now. “But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that it made sense to do a round now rather than wait for the A. The whole legal industry is undergoing radical change and we want to be leading it.”

Juro’s SaaS product is an integrated contracts workflow that combines contract creation, e-signing and commenting capabilities with AI-powered contract analytics.

Its general focus is on customers that have to manage a high volume of contacts — such as marketplaces.

The 2016-founded startup is not breaking out any customer numbers yet but says its client list includes the likes of Estee Lauder, Deliveroo and Nested. And Mabey adds that “most” of its demand is coming from enterprise at this point, noting it has “several tech unicorns and Fortune 500 companies in trial”.

While design is clearly a major focus — with the startup deploying clean-looking templates and visual cues to offer a user-friendly ‘upgrade’ on traditional legal processes — the machine learning component is its scalable, value-added differentiator to serve the target b2b users by helping them identify recurring sticking points in contract negotiations and keep on top of contract renewals.

Mabey tells TechCrunch the new funding will be used to double down on development of the machine learning component of the product.

“We’re not the first to market in contract management by about 25 years,” he says with a smilie. “So we have always needed to prove out our vision of why the incumbents are failing. One part of this is clunky UX and we’ve succeeded so far in replacing legacy providers through better design (e.g. we replace DocuSign at 80% of our customers).

“But the thing we and our investors are really excited about is not just helping businesses with contract workflow but helping them understand their contract data, auto-tag contracts, see pattens in negotiations and red flag unusual contract terms.”

While this machine learning element is where he sees Juro cutting out a competitive edge in an existing and established market, Mabey concedes it takes “quite a lot of capital to do well”. Hence taking more funding now.

“We need a level of predictive accuracy in our models that risk averse lawyers can get comfortable with and that’s a big ask!” he says.

Specifically, Juro will be using the funding to hire data scientists and machine learning engineers — building out the team at both its London and Riga offices. “We’re doing it like crazy,” adds Mabey. “For example, we just hired from the UK government Digital Service the data scientist who delivered the first ML model used by the UK government (on the gov.uk website).

“There is a huge opportunity here but great execution is key and we’re building a world class team to do it. It’s a big bet to grow revenue as quickly as we are and do this kind of R&D but that’s just what the market is demanding.”

Juro’s HQ remains in London for now, though Mabey notes its entire engineering team is based in the EU — between Riga, Amsterdam and Barcelona — “in part to avoid ‘Brexit risk’”.

“Only 27% of the team is British and we have customers operating in 12 countries — something I’m quite proud of — but it does leave us rather exposed. We’re very open minded about where we will be based in the future and are waiting to hear from the government on the final terms of Brexit,” he says when asked whether the startup has any plans to Brexit to Berlin.

“We always look beyond the UK for talent: if the government cannot provide certainty to our Romanian product designer (ex Kalo, Entrepreneur First) that she can stay in the UK post Brexit without risking a visa application, tbh it makes me less bullish on London!”

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