Feb
19
2020
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SentinelOne raises $200M at a $1.1B valuation to expand its AI-based endpoint security platform

As cybercrime continues to evolve and expand, a startup that is building a business focused on endpoint security has raised a big round of funding. SentinelOne — which provides a machine learning-based solution for monitoring and securing laptops, phones, containerised applications and the many other devices and services connected to a network — has picked up $200 million, a Series E round of funding that it says catapults its valuation to $1.1 billion.

The funding is notable not just for its size but for its velocity: it comes just eight months after SentinelOne announced a Series D of $120 million, which at the time valued the company around $500 million. In other words, the company has more than doubled its valuation in less than a year — a sign of the cybersecurity times.

This latest round is being led by Insight Partners, with Tiger Global Management, Qualcomm Ventures LLC, Vista Public Strategies of Vista Equity Partners, Third Point Ventures and other undisclosed previous investors all participating.

Tomer Weingarten, CEO and co-founder of the company, said in an interview that while this round gives SentinelOne the flexibility to remain in “startup” mode (privately funded) for some time — especially since it came so quickly on the heels of the previous large round — an IPO “would be the next logical step” for the company. “But we’re not in any rush,” he added. “We have one to two years of growth left as a private company.”

While cybercrime is proving to be a very expensive business (or very lucrative, I guess, depending on which side of the equation you sit on), it has also meant that the market for cybersecurity has significantly expanded.

Endpoint security, the area where SentinelOne concentrates its efforts, last year was estimated to be around an $8 billion market, and analysts project that it could be worth as much as $18.4 billion by 2024.

Driving it is the single biggest trend that has changed the world of work in the last decade. Everyone — whether a road warrior or a desk-based administrator or strategist, a contractor or full-time employee, a front-line sales assistant or back-end engineer or executive — is now connected to the company network, often with more than one device. And that’s before you consider the various other “endpoints” that might be connected to a network, including machines, containers and more. The result is a spaghetti of a problem. One survey from LogMeIn, disconcertingly, even found that some 30% of IT managers couldn’t identify just how many endpoints they managed.

“The proliferation of devices and the expanding network are the biggest issues today,” said Weingarten. “The landscape is expanding and it is getting very hard to monitor not just what your network looks like but what your attackers are looking for.”

This is where an AI-based solution like SentinelOne’s comes into play. The company has roots in the Israeli cyberintelligence community but is based out of Mountain View, and its platform is built around the idea of working automatically not just to detect endpoints and their vulnerabilities, but to apply behavioral models, and various modes of protection, detection and response in one go — in a product that it calls its Singularity Platform that works across the entire edge of the network.

“We are seeing more automated and real-time attacks that themselves are using more machine learning,” Weingarten said. “That translates to the fact that you need defence that moves in real time as with as much automation as possible.”

SentinelOne is by no means the only company working in the space of endpoint protection. Others in the space include Microsoft, CrowdStrike, Kaspersky, McAfee, Symantec and many others.

But nonetheless, its product has seen strong uptake to date. It currently has some 3,500 customers, including three of the biggest companies in the world, and “hundreds” from the global 2,000 enterprises, with what it says has been 113% year-on-year new bookings growth, revenue growth of 104% year-on-year and 150% growth year-on-year in transactions over $2 million. It has 500 employees today and plans to hire up to 700 by the end of this year.

One of the key differentiators is the focus on using AI, and using it at scale to help mitigate an increasingly complex threat landscape, to take endpoint security to the next level.

“Competition in the endpoint market has cleared with a select few exhibiting the necessary vision and technology to flourish in an increasingly volatile threat landscape,” said Teddie Wardi, managing director of Insight Partners, in a statement. “As evidenced by our ongoing financial commitment to SentinelOne along with the resources of Insight Onsite, our business strategy and ScaleUp division, we are confident that SentinelOne has an enormous opportunity to be a market leader in the cybersecurity space.”

Weingarten said that SentinelOne “gets approached every year” to be acquired, although he didn’t name any names. Nevertheless, that also points to the bigger consolidation trend that will be interesting to watch as the company grows. SentinelOne has never made an acquisition to date, but it’s hard to ignore that, as the company to expand its products and features, that it might tap into the wider market to bring in other kinds of technology into its stack.

“There are definitely a lot of security companies out there,” Weingarten noted. “Those that serve a very specific market are the targets for consolidation.”

Jan
30
2020
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OpsRamp raises $37.5M for its hybrid IT operations platform

OpsRamp, a service that helps IT teams discover, monitor, manage and — maybe most importantly — automate their hybrid environments, today announced that it has closed a $37.5 million funding round led by Morgan Stanley Expansion Capital, with participation from existing investor Sapphire Ventures and new investor Hewlett Packard Enterprise.

OpsRamp last raised funding in 2017, when Sapphire led its $20 million Series A round.

At the core of OpsRamp’s services is its AIOps platform. Using machine learning and other techniques, this service aims to help IT teams manage increasingly complex infrastructure deployments, provide intelligent alerting and eventually automate more of their tasks. The company’s overall product portfolio also includes tools for cloud monitoring and incident management.

The company says its annual recurrent revenue increased by 300% in 2019 (though we obviously don’t know what number it started 2019 with). In total, OpsRamp says it now has 1,400 customers on its platform and alliances with AWS, ServiceNow, Google Cloud Platform and Microsoft Azure.

OpsRamp co-founder and CEO Varma Kunaparaju

According to OpsRamp co-founder and CEO Varma Kunaparaju, most of the company’s customers are mid to large enterprises. “These IT teams have large, complex, hybrid IT environments and need help to simplify and consolidate an incredibly fragmented, distributed and overwhelming technology and infrastructure stack,” he said. “The company is also seeing success in the ability of our partners to help us reach global enterprises and Fortune 5000 customers.”

Kunaparaju told me that the company plans to use the new funding to expand its go-to-market efforts and product offerings. “The company will be using the money in a few different areas, including expanding our go-to-market motion and new pursuits in EMEA and APAC, in addition to expanding our North American presence,” he said. “We’ll also be doubling-down on product development on a variety of fronts.”

Given that hybrid clouds only increase the workload for IT organizations and introduce additional tools, it’s maybe no surprise that investors are now interested in companies that offer services that rein in this complexity. If anything, we’ll likely see more deals like this one in the coming months.

“As more of our customers transition to hybrid infrastructure, we find the OpsRamp platform to be a differentiated IT operations management offering that aligns well with the core strategies of HPE,” said Paul Glaser, vice president and head of Hewlett Packard Pathfinder. “With OpsRamp’s product vision and customer traction, we felt it was the right time to invest in the growth and scale of their business.”

Jan
28
2020
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RealityEngines launches its autonomous AI service

RealityEngines.AI, an AI and machine learning startup founded by a number of former Google executives and engineers, is coming out of stealth today and announcing its first set of products.

When the company first announced its $5.25 million seed round last year, CEO Bindu Reddy wasn’t quite ready to disclose RealityEngines’ mission beyond saying that it planned to make machine learning easier for enterprises. With today’s launch, the team is putting this into practice by launching a set of tools that specifically tackle a number of standard enterprise use cases for ML, including user churn predictions, fraud detection, sales lead forecasting, security threat detection and cloud spend optimization. For use cases that don’t fit neatly into these buckets, the service also offers a more general predictive modeling service.

Before co-founding RealiyEngines, Reddy was the head of product for Google Apps and general manager for AI verticals at AWS. Her co-founders are Arvind Sundararajan (formerly at Google and Uber) and Siddartha Naidu (who founded BigQuery at Google). Investors in the company include Eric Schmidt, Ram Shriram, Khosla Ventures and Paul Buchheit.

As Reddy noted, the idea behind this first set of products from RealityEngines is to give businesses an easy entry into machine learning, even if they don’t have data scientists on staff.

Besides talent, another issue that businesses often face is that they don’t always have massive amounts of data to train their networks effectively. That has long been a roadblock for many companies that want to see what AI can do for them but that didn’t have the right resources to do so. RealityEngines overcomes this by creating realistic synthetic data that it can then use to augment a company’s existing data. In its tests, this creates models that are up to 15% more accurate than models that were trained without the synthetic data.

“The most prominent use of generative adversarial networks — GANS — has been to create deepfakes,” said Reddy. “Deepfakes have captured the public’s imagination by highlighting how easy it to spread misinformation with these doctored videos and images. However, GANS can also be applied to productive and good use. They can be used to create synthetic data sets which when then be combined with the original data, to produce robust AI models even when a business doesn’t have much training data.”

RealityEngines currently has about 20 employees, most of whom have a deep background in ML/AI, both as researchers and practitioners.

Jan
13
2020
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Google brings IBM Power Systems to its cloud

As Google Cloud looks to convince more enterprises to move to its platform, it needs to be able to give businesses an onramp for their existing legacy infrastructure and workloads that they can’t easily replace or move to the cloud. A lot of those workloads run on IBM Power Systems with their Power processors, and, until now, IBM was essentially the only vendor that offered cloud-based Power systems. Now, however, Google is also getting into this game by partnering with IBM to launch IBM Power Systems on Google Cloud.

“Enterprises looking to the cloud to modernize their existing infrastructure and streamline their business processes have many options,” writes Kevin Ichhpurani, Google Cloud’s corporate VP for its global ecosystem, in today’s announcement. “At one end of the spectrum, some organizations are re-platforming entire legacy systems to adopt the cloud. Many others, however, want to continue leveraging their existing infrastructure while still benefiting from the cloud’s flexible consumption model, scalability, and new advancements in areas like artificial intelligence, machine learning, and analytics.”

Power Systems support obviously fits in well here, given that many companies use them for mission-critical workloads based on SAP and Oracle applications and databases. With this, they can take those workloads and slowly move them to the cloud, without having to re-engineer their applications and infrastructure. Power Systems on Google Cloud is obviously integrated with Google’s services and billing tools.

This is very much an enterprise offering, without a published pricing sheet. Chances are, given the cost of a Power-based server, you’re not looking at a bargain, per-minute price here.

Because IBM has its own cloud offering, it’s a bit odd to see it work with Google to bring its servers to a competing cloud — though it surely wants to sell more Power servers. The move makes perfect sense for Google Cloud, though, which is on a mission to bring more enterprise workloads to its platform. Any roadblock the company can remove works in its favor, and, as enterprises get comfortable with its platform, they’ll likely bring other workloads to it over time.

Jan
09
2020
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Sisense nabs $100M at a $1B+ valuation for accessible big data business analytics

Sisense, an enterprise startup that has built a business analytics business out of the premise of making big data as accessible as possible to users — whether it be through graphics on mobile or desktop apps, or spoken through Alexa — is announcing a big round of funding today and a large jump in valuation to underscore its traction. The company has picked up $100 million in a growth round of funding that catapults Sisense’s valuation to over $1 billion, funding that it plans to use to continue building out its tech, as well as for sales, marketing and development efforts.

For context, this is a huge jump: The company was valued at only around $325 million in 2016 when it raised a Series E, according to PitchBook. (It did not disclose valuation in 2018, when it raised a venture round of $80 million.) It now has some 2,000 customers, including Tinder, Philips, Nasdaq and the Salvation Army.

This latest round is being led by the high-profile enterprise investor Insight Venture Partners, with Access Industries, Bessemer Venture Partners, Battery Ventures, DFJ Growth and others also participating. The Access investment was made via Claltech in Israel, and it seems that this led to some details of this getting leaked out as rumors in recent days. Insight is in the news today for another big deal: Wearing its private equity hat, the firm acquired Veeam for $5 billion. (And that speaks to a particular kind of trajectory for enterprise companies that the firm backs: Veeam had already been a part of Insight’s venture portfolio.)

Mature enterprise startups have proven their business cases are going to be an ongoing theme in this year’s fundraising stories, and Sisense is part of that theme, with annual recurring revenues of over $100 million speaking to its stability and current strength. The company has also made some key acquisitions to boost its business, such as the acquisition of Periscope Data last year (coincidentally, also for $100 million, I understand).

Its rise also speaks to a different kind of trend in the market: In the wider world of business intelligence, there is an increasing demand for more digestible data in order to better tap advances in data analytics to use it across organizations. This was also one of the big reasons why Salesforce gobbled up Tableau last year for a slightly higher price: $15.7 billion.

Sisense, bringing in both sleek end user products but also a strong theme of harnessing the latest developments in areas like machine learning and AI to crunch the data and order it in the first place, represents a smaller and more fleet of foot alternative for its customers. “We found a way to make accessing data extremely simple, mashing it together in a logical way and embedding it in every logical place,” explained CEO Amir Orad to us in 2018.

“We have enjoyed watching the Sisense momentum in the past 12 months, the traction from its customers as well as from industry leading analysts for the company’s cloud native platform and new AI capabilities. That coupled with seeing more traction and success with leading companies in our portfolio and outside, led us to want to continue and grow our relationship with the company and lead this funding round,” said Jeff Horing, managing director at Insight Venture Partners, in a statement.

To note, Access Industries is an interesting backer which might also potentially shape up to be strategic, given its ownership of Warner Music Group, Alibaba, Facebook, Square, Spotify, Deezer, Snap and Zalando.

“Given our investments in market leading companies across diverse industries, we realize the value in analytics and machine learning and we could not be more excited about Sisense’s trajectory and traction in the market,” added Claltech’s Daniel Shinar in a statement.

Dec
12
2019
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DataRobot is acquiring Paxata to add data prep to machine learning platform

DataRobot, a company best known for creating automated machine learning models known as AutoML, announced today that it intends to acquire Paxata, a data prep platform startup. The companies did not reveal the purchase price.

Paxata raised a total of $90 million before today’s acquisition, according to the company.

Up until now, DataRobot has concentrated mostly on the machine learning and data science aspect of the workflow — building and testing the model, then putting it into production. The data prep was left to other vendors like Paxata, but DataRobot, which raised $206 million in September, saw an opportunity to fill in a gap in their platform with Paxata.

“We’ve identified, because we’ve been focused on machine learning for so long, a number of key data prep capabilities that are required for machine learning to be successful. And so we see an opportunity to really build out a unique and compelling data prep for machine learning offering that’s powered by the Paxata product, but takes the knowledge and understanding and the integration with the machine learning platform from DataRobot,” Phil Gurbacki, SVP of product development and customer experience at DataRobot, told TechCrunch.

Prakash Nanduri, CEO and co-founder at Paxata, says the two companies were a great fit and it made a lot of sense to come together. “DataRobot has got a significant number of customers, and every one of their customers have a data and information management problem. For us, the deal allows us to rapidly increase the number of customers that are able to go from data to value. By coming together, the value to the customer is increased at an exponential level,” he explained.

DataRobot is based in Boston, while Paxata is in Redwood City, Calif. The plan moving forward is to make Paxata a west coast office, and all of the company’s almost 100 employees will become part of DataRobot when the deal closes.

While the two companies are working together to integrate Paxata more fully into the DataRobot platform, the companies also plan to let Paxata continue to exist as a standalone product.

DataRobot has raised more than $431 million, according to PitchBook data. It raised $206 million of that in its last round. At the time, the company indicated it would be looking for acquisition opportunities when it made sense.

This match-up seems particularly good, given how well the two companies’ capabilities complement one another, and how much customer overlap they have. The deal is expected to close before the end of the year.

Dec
09
2019
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AWS is sick of waiting for your company to move to the cloud

AWS held its annual re:Invent customer conference last week in Las Vegas. Being Vegas, there was pageantry aplenty, of course, but this year’s model felt a bit different than in years past, lacking the onslaught of major announcements we are used to getting at this event.

Perhaps the pace of innovation could finally be slowing, but the company still had a few messages for attendees. For starters, AWS CEO Andy Jassy made it clear he’s tired of the slow pace of change inside the enterprise. In Jassy’s view, the time for incremental change is over, and it’s time to start moving to the cloud faster.

AWS also placed a couple of big bets this year in Vegas to help make that happen. The first involves AI and machine learning. The second, moving computing to the edge, closer to the business than the traditional cloud allows.

The question is what is driving these strategies? AWS had a clear head start in the cloud, and owns a third of the market, more than double its closest rival, Microsoft. The good news is that the market is still growing and will continue to do so for the foreseeable future. The bad news for AWS is that it can probably see Google and Microsoft beginning to resonate with more customers, and it’s looking for new ways to get a piece of the untapped part of the market to choose AWS.

Dec
02
2019
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New Amazon tool simplifies delivery of containerized machine learning models

As part of the flurry of announcements coming this week out of AWS re:Invent, Amazon announced the release of Amazon SageMaker Operators for Kubernetes, a way for data scientists and developers to simplify training, tuning and deploying containerized machine learning models.

Packaging machine learning models in containers can help put them to work inside organizations faster, but getting there often requires a lot of extra management to make it all work. Amazon SageMaker Operators for Kubernetes is supposed to make it easier to run and manage those containers, the underlying infrastructure needed to run the models and the workflows associated with all of it.

“While Kubernetes gives customers control and portability, running ML workloads on a Kubernetes cluster brings unique challenges. For example, the underlying infrastructure requires additional management such as optimizing for utilization, cost and performance; complying with appropriate security and regulatory requirements; and ensuring high availability and reliability,” AWS’ Aditya Bindal wrote in a blog post introducing the new feature.

When you combine that with the workflows associated with delivering a machine learning model inside an organization at scale, it becomes part of a much bigger delivery pipeline, one that is challenging to manage across departments and a variety of resource requirements.

This is precisely what Amazon SageMaker Operators for Kubernetes has been designed to help DevOps teams do. “Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale,” Bindal wrote.

The promise of Kubernetes is that it can orchestrate the delivery of containers at the right moment, but if you haven’t automated delivery of the underlying infrastructure, you can over (or under) provision and not provide the correct amount of resources required to run the job. That’s where this new tool, combined with SageMaker, can help.

“With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near 100% utilization,” Bindal wrote.

Amazon SageMaker Operators for Kubernetes are available today in select AWS regions.

Nov
26
2019
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New Amazon capabilities put machine learning in reach of more developers

Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.

Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

Nov
20
2019
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Google Cloud launches Bare Metal Solution

Google Cloud today announced the launch of a new bare metal service, dubbed the Bare Metal Solution. We aren’t talking about bare metal servers offered directly by Google Cloud here, though. Instead, we’re talking about a solution that enterprises can use to run their specialized workloads on certified hardware that’s co-located in the Google Cloud data centers and directly connect them to Google Cloud’s suite of other services. The main workload that makes sense for this kind of setup is databases, Google notes, and specifically Oracle Database.

Bare Metal Solution is, as the name implies, a fully integrated and fully managed solution for setting up this kind of infrastructure. It involves a completely managed hardware infrastructure that includes servers and the rest of the data center facilities like power and cooling, support contracts with Google Cloud and billing are handled through Google’s systems, as well as an SLA. The software that’s deployed on those machines is managed by the customer — not Google.

The overall idea, though, is clearly to make it easier for enterprises with specialized workloads that can’t easily be migrated to the cloud to still benefit from the cloud-based services that need access to the data from these systems. Machine learning is an obvious example, but Google also notes that this provides these companies with a bridge to slowly modernize their tech infrastructure in general (where ‘modernize’ tends to mean ‘move to the cloud’).

“These specialized workloads often require certified hardware and complicated licensing and support agreements,” Google writes. “This solution provides a path to modernize your application infrastructure landscape, while maintaining your existing investments and architecture. With Bare Metal Solution, you can bring your specialized workloads to Google Cloud, allowing you access and integration with GCP services with minimal latency.”

Since this service is co-located with Google Cloud, there are no separate ingress and egress charges for data that moves between Bare Metal Solution and Google Cloud in the same region.

The servers for this solution, which are certified to run a wide range of applications (including Oracle Database) range from dual-socket 16-core systems with 384 GB of RAM to quad-socket servers with 112 cores and 3072 GB of RAM. Pricing is on a monthly basis, with a preferred term length of 36 months.

Obviously, this isn’t the kind of solution that you self-provision, so the only way to get started — and get pricing information — is to talk to Google’s sales team. But this is clearly the kind of service that we should expect from Google Cloud, which is heavily focused on providing as many enterprise-ready services as possible.

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