RealityEngines.AI raises $5.25M seed round to make ML easier for enterprises

RealityEngines.AI, a research startup that wants to help enterprises make better use of AI, even when they only have incomplete data, today announced that it has raised a $5.25 million seed funding round. The round was led by former Google CEO and Chairman Eric Schmidt and Google founding board member Ram Shriram. Khosla Ventures, Paul Buchheit, Deepchand Nishar, Elad Gil, Keval Desai, Don Burnette and others also participated in this round.

The fact that the service was able to raise from this rather prominent group of investors clearly shows that its overall thesis resonates. The company, which doesn’t have a product yet, tells me that it specifically wants to help enterprises make better use of the smaller and noisier datasets they have and provide them with state-of-the-art machine learning and AI systems that they can quickly take into production. It also aims to provide its customers with systems that can explain their predictions and are free of various forms of bias, something that’s hard to do when the system is essentially a black box.

As RealityEngines CEO Bindu Reddy, who was previously the head of products for Google Apps, told me the company plans to use the funding to build out its research and development team. The company, after all, is tackling some of the most fundamental and hardest problems in machine learning right now — and that costs money. Some, like working with smaller datasets, already have some available solutions like generative adversarial networks that can augment existing datasets and that RealityEngines expects to innovate on.

Reddy is also betting on reinforcement learning as one of the core machine learning techniques for the platform.

Once it has its product in place, the plan is to make it available as a pay-as-you-go managed service that will make machine learning more accessible to large enterprise, but also to small and medium businesses, which also increasingly need access to these tools to remain competitive.


By Frederic Lardinois

AWS is now making Amazon Personalize available to all customers

Amazon Personalize, first announced during AWS re:Invent last November, is now available to all Amazon Web Services customers. The API enables developers to add custom machine learning models to their apps, including ones for personalized product recommendations, search results and direct marketing, even if they don’t have machine learning experience.

The API processes data using algorithms originally created for Amazon’s own retail business,  but the company says all data will be “kept completely private, owned entirely by the customer.” The service is now available to AWS users in three U.S. regions, East (Ohio), East (North Virginia) and West (Oregon), two Asia Pacific regions (Tokyo and Singapore) and Ireland in the European Union, with more regions to launch soon.

AWS customers who have already added Amazon Personalize to their apps include Yamaha Corporation of America, Subway, Zola and Segment. In Amazon’s press release, Yamaha Corporation of America Director of Information Technology Ishwar Bharbhari said Amazon Personalize “saves us up to 60% of the time needed to set up and tune the infrastructure and algorithms for our machine learning models when compared to building and configuring the environment on our own.”

Amazon Personalize’s pricing model charges five cents per GB of data uploaded to Amazon Personalize and 24 cents per training hour used to train a custom model with their data. Real-time recommendation requests are priced based on how many are uploaded, with discounts for larger orders.


By Catherine Shu

Qubole launches Quantum, its serverless database engine

Qubole, the data platform founded by Apache Hive creator and former head of Facebook’s Data Infrastructure team Ashish Thusoo, today announced the launch of Quantum, its first serverless offering.

Qubole may not necessarily be a household name, but its customers include the likes of Autodesk, Comcast, Lyft, Nextdoor and Zillow . For these users, Qubole has long offered a self-service platform that allowed their data scientists and engineers to build their AI, machine learning and analytics workflows on the public cloud of their choice. The platform sits on top of open-source technologies like Apache Spark, Presto and Kafka, for example.

Typically, enterprises have to provision a considerable amount of resources to give these platforms the resources they need. These resources often go unused and the infrastructure can quickly become complex.

Qubole already abstracts most of this away and offering what is essentially a serverless platform. With Quantum, however, it is going a step further by launching a high-performance serverless SQP engine that allows users to query petabytes of data with nothing else by ANSI-SQL, given them the choice between using a Presto cluster or a serverless SQL engine to run their queries, for example.

The data can be stored on AWS, Azure, Google cloud or Oracle Cloud and users won’t have to set up a second data lake or move their data to another platform to use the SQL engine. Quantum automatically scales up or down as needed, of course, and users can still work with the same metastore for their data, no matter whether they choose the clustered or serverless option. Indeed, Quantum is essentially just another SQL engine without Qubole’s overall suite of engines.

Typically, Qubole charges enterprises by compute minutes. When using Quantum, the company uses the same metric, but enterprises pay for the execution time of the query. “So instead of the Qubole compute units being associated with the number of minutes the cluster was up and running, it is associated with the Qubole compute units consumed by that particular query or that particular workload, which is even more fine-grained ” Thusoo explained. “This works really well when you have to do interactive workloads.”

Thusoo notes that Quantum is targeted at analysts who often need to perform interactive queries on data stored in object stores. Qubole integrates with services like Tableau and Looker (which Google is now in the process of acquiring). “They suddenly get access to very elastic compute capacity, but they are able to come through a very familiar user interface,” Thusoo noted.

 


By Frederic Lardinois

How we scaled our startup by being remote first

Startups are often associated with the benefits and toys provided in their offices. Foosball tables! Free food! Dog friendly! But what if the future of startups was less about physical office space and more about remote-first work environments? What if, in fact, the most compelling aspect of a startup work environment is that the employees don’t have to go to one?

A remote-first company model has been Seeq’s strategy since our founding in 2013. We have raised $35 million and grown to more than 100 employees around the globe. Remote-first is clearly working for us and may be the best model for other software companies as well.

So, who is Seeq and what’s been the key to making the remote-first model work for us?  And why did we do it in the first place?

Seeq is a remote-first startup – i.e. it was founded with the intention of not having a physical headquarters or offices, and still operates that way – that is developing an advanced analytics application that enables process engineers and subject matter experts in oil & gas, pharmaceuticals, utilities, and other process manufacturing industries to investigate and publish insights from the massive amounts of sensor data they generate and store.

To succeed, we needed to build a team quickly with two skill sets: 1) software development expertise, including machine learning, AI, data visualization, open source, agile development processes, cloud, etc. and 2) deep domain expertise in the industries we target.

Which means there is no one location where we can hire all the employees we need: Silicon Valley for software, Houston for oil & gas, New Jersey for fine chemicals, Seattle for cloud expertise, water utilizes across the country, and so forth. But being remote-first gives has made recruiting and hiring these high-demand roles easier much easier than if we were collocated.

Image via Seeq Corporation

Job postings on remote-specific web sites like FlexJobs, Remote.co and Remote OK typically draw hundreds of applicants in a matter of days. This enables Seeq to hire great employees who might not call Seattle, Houston or Silicon Valley home – and is particularly attractive to employees with location-dependent spouses or employees who simply want to work where they want to live.

But a remote-first strategy and hiring quality employees for the skills you need is not enough: succeeding as a remote-first company requires a plan and execution around the “3 C’s of remote-first”.

The three requirements to remote-first success are the three C’s: communication, commitment and culture.


By Arman Tabatabai

Health[at]Scale lands $16M Series A to bring machine learning to healthcare

Health[at]Scale, a startup with founders who have both medical and engineering expertise, wants to bring machine learning to bear on healthcare treatment options to produce outcomes with better results and less aftercare. Today the company announced a $16 million Series A. Optum, which is part of the UnitedHealth Group, was the sole investor .

Today, when people looks at treatment options, they may look at a particular surgeon or hospital, or simply what the insurance company will cover, but they typically lack the data to make truly informed decisions. This is true across every part of the healthcare system, particularly in the U.S. The company believes using machine learning, it can produce better results.

“We are a machine learning shop, and we focus on what I would describe as precision delivery. So in other words, we look at this question of how do we match patients to the right treatments, by the right providers, at the right time,” Zeeshan Syed, Health at Scale CEO told TechCrunch.

The founders see the current system as fundamentally flawed, and while they see their customers as insurance companies, hospital systems and self-insured employers; they say the tools they are putting into the system should help everyone in the loop get a better outcome.

The idea is to make treatment decisions more data driven. While they aren’t sharing their data sources, they say they have information from patients with a given condition, to doctors who treat that condition, to facilities where the treatment happens. By looking at a patient’s individual treatment needs and medical history, they believe they can do a better job of matching that person to the best doctor and hospital for the job. They say this will result in the fewest post-operative treatment requirements, whether that involves trips to the emergency room or time in a skilled nursing facility, all of which would end up adding significant additional cost.

If you’re thinking this is strictly about cost savings for these large institutions, Mohammed Saeed, who is the company’s chief medical officer and has and MD from Harvard and a PhD in electrical engineering from MIT, insists that isn’t the case. “From our perspective, it’s a win-win situation since we provide the best recommendations that have the patient interest at heart, but from a payer or provider perspective, when you have lower complication rates you have better outcomes and you lower your total cost of care long term,” he said.

The company says the solution is being used by large hospital systems and insurer customers, although it couldn’t share any. The founders also said, it has studied the outcomes after using its software and the machine learning models have produced better outcomes, although it couldn’t provide the data to back that up at that point at this time.

The company was founded in 2015 and currently has 11 employees. It plans to use today’s funding to build out sales and marketing to bring the solution to a wider customer set.


By Ron Miller

Unveiling its latest cohort, Alchemist announces $4 million in funding for its enterprise accelerator

The enterprise software and services-focused accelerator Alchemist has raised $4 million in fresh financing from investors BASF and the Qatar Development Bank, just in time for its latest demo day unveiling 20 new companies.

Qatar and BASF join previous investors, including the venture firms Mayfield, Khosla Ventures, Foundation Capital, DFJ and USVP, and corporate investors like Cisco, Siemens and Juniper Networks.

While the roster of successes from Alchemist’s fund isn’t as lengthy as Y Combinator, the accelerator program has launched the likes of the quantum computing upstart Rigetti, the soft-launch developer tool LaunchDarkly and drone startup Matternet .

Some (personal) highlights of the latest cohort include:

  • Bayware: Helmed by a former head of software-defined networking from Cisco, the company is pitching a tool that makes creating networks in multi-cloud environments as easy as copying and pasting.
  • MotorCortex.AI: Co-founded by a Stanford engineering professor and a Carnegie Mellon roboticist, the company is using computer vision, machine learning and robotics to create a fruit packer for packaging lines. Starting with avocados, the company is aiming to tackle the entire packaging side of pick and pack in logistics.
  • Resilio: With claims of a 96% effectiveness rate and $35,000 in annual recurring revenue with another $1 million in the pipeline, Resilio is already seeing companies embrace its mobile app that uses a phone’s camera to track stress levels and application-based prompts on how to lower it, according to Alchemist.
  • Operant Networks: It’s a long-held belief (of mine) that if computing networks are already irrevocably compromised, the best thing that companies and individuals can do is just encrypt the hell out of their data. Apparently Operant agrees with me. The company is claiming 50% time savings with this approach, and have booked $1.9 million in 2019 as proof, according to Alchemist.
  • HPC Hub: HPC Hub wants to democratize access to supercomputers by overlaying a virtualization layer and pre-installed software on underutilized super computers to give more companies and researchers easier access to machines… and they’ve booked $92,000 worth of annual recurring revenue.
  • DinoPlusAI: This chip developer is designing a low latency chip for artificial intelligence applications, reducing latency by 12 times over a competing Nvidia chip, according to the company. DinoPlusAI sees applications for its tech in things like real-time AI markets and autonomous driving. Its team is led by a designer from Cadence and Broadcom and the company already has $8 million in letters of intent signed, according to Alchemist.
  • Aero Systems West: Co-founders from the Air Force’s Research Labs and MIT are aiming to take humans out of drone operations and maintenance. The company contends that for every hour of flight time, drones require seven hours of maintenance and check ups. Aero Systems aims to reduce that by using remote analytics, self-inspection, autonomous deployment and automated maintenance to take humans out of the drone business.

Watch a live stream of Alchemist’s demo day pitches, starting at 3PM, here.

 


By Jonathan Shieber

Beyond costs, what else can we do to make housing affordable?

This week on Extra Crunch, I am exploring innovations in inclusive housing, looking at how 200+ companies are creating more access and affordability. Yesterday, I focused on startups trying to lower the costs of housing, from property acquisition to management and operations.

Today, I want to focus on innovations that improve housing inclusion more generally, such as efforts to pair housing with transit, small business creation, and mental rehabilitation. These include social impact-focused interventions, interventions that increase income and mobility, and ecosystem-builders in housing innovation.

Nonprofits and social enterprises lead many of these innovations. Yet because these areas are perceived to be not as lucrative, fewer technologists and other professionals have entered them. New business models and technologies have the opportunity to scale many of these alternative institutions — and create tremendous social value. Social impact is increasingly important to millennials, with brands like Patagonia having created loyal fan bases through purpose-driven leadership.

While each of these sections could be their own market map, this overall market map serves as an initial guide to each of these spaces.

Social impact innovations

These innovations address:


By Arman Tabatabai

Algorithmia raises $25M Series B for its AI automation platform

Algorithmia, a Seattle-based startup that offers a cloud-agnostic AI automation platform for enterprises, today announced a $25 million Series B funding round led by Norwest Partners. Madrona, Gradient Ventures, Work-Bench, Osage University Partners and Rakuten Ventures also participated in this round.

While the company started out five years ago as a marketplace for algorithms, it now mostly focuses on machine learning and helping enterprises take their models into production.

“It’s actually really hard to productionize machine learning models,” Algorithmia CEO Diego Oppenheimer told me. “It’s hard to help data scientists to not deal with data infrastructure but really being able to build out their machine learning and AI muscle.”

To help them, Algorithmia essentially built out a machine learning DevOps platform that allows data scientists to train their models on the platform and with the framework of their choice, bring it to Algorithmia — a platform that has already been blessed by their IT departments — and take it into production.

“Every Fortune 500 CIO has an AI initiative but they are bogged down by the difficulty of managing and deploying ML models,” said Rama Sekhar, a partner at Norwest Venture Partners, who has now joined the company’s board. “Algorithmia is the clear leader in building the tools to manage the complete machine learning lifecycle and helping customers unlock value from their R&D investments.”

With the new funding, the company will double down on this focus by investing in product development to solve these issues, but also by building out its team, with a plan to double its headcount over the next year. A year from now, Oppenheimer told me, he hopes that Algorithmia will be a household name for data scientists and, maybe more importantly, their platform of choice for putting their models into production.

“How does Algorithmia succeed? Algorithmia succeeds when our customers are able to deploy AI and ML applications,” Oppenheimer said. “And although there is a ton of excitement around doing this, the fact is that it’s really difficult for companies to do so.”

The company previously raised a $10.5 million Series A round led by Google’s AI fund. It’s customers now include the United Nations, a number of U.S. intelligence agencies and Fortune 500 companies. In total, over 90,000 engineers and data scientists are now on the platform.


By Frederic Lardinois

Announcing TechCrunch Sessions: Enterprise this September in San Francisco

Of the many categories in the tech world, none is more ferociously competitive than enterprise. For decades, SAP, Oracle, Adobe, Microsoft, IBM and Salesforce, to name a few of the giants, have battled to deliver the tools businesses want to become more productive and competitive. That market is closing in on $500 billion in sales per year, which explains why hundreds of new enterprise startups launch every year and dozens are acquired by the big incumbents trying to maintain their edge.

Last year alone, the top 10 enterprise acquisitions were worth $87 billion and included IBM’s acquiring Red Hat for $34 billion, SAP paying $8 billion for Qualtrics, Microsoft landing GitHub for $7.5 billion, Salesforce acquiring MuleSoft for $6.5 billion and Adobe grabbing Marketo for $4.75 billion. No startup category has made more VCs and founders wildly wealthy, and none has seen more mighty companies rise faster or fall harder. That technology and business thrill ride makes enterprise a category TechCrunch has long wanted to tackle head on.

TC Sessions: Enterprise (September 5 at San Francisco’s Yerba Buena Center) will take on the big challenges and promise facing enterprise companies today. TechCrunch’s editors, notably Frederic Lardinois, Ron Miller and Connie Loizos, will bring to the stage founders and leaders from established and emerging companies to address rising questions like the promised revolution from machine learning and AI, intelligent marketing automation and the inevitability of the cloud, as well as the outer reaches of technology, like quantum and blockchain.

We’ll enlist proven enterprise-focused VCs to reveal where they are directing their early, middle and late-stage investments. And we’ll ask the most proven serial entrepreneurs to tell us what it really took to build that company, and which company they would like to create next. All throughout the show, TechCrunch’s editors will zero in on emerging enterprise technologies to sort the hype from the reality. Whether you are a founder, an investor, enterprise-minded engineer or a corporate CTO / CIO, TC Sessions: Enterprise will provide a valuable day of new insights and great networking.

Tickets are now available for purchase on our website at the early-bird rate of $395. Want to bring a group of people from your company? Get an automatic 15% savings when you purchase four or more tickets at once. Are you an early-stage startup? We have a limited number of Startup Demo Packages available for $2,000, which includes four tickets to attend the event. Students are invited to apply for a reduced-price student ticket at just $245. Additionally, for each ticket purchased for TC Sessions: Enterprise, you will also be registered for a complimentary Expo Only pass to TechCrunch Disrupt SF on October 2-4.

Interested in sponsoring TC Sessions: Enterprise? Fill out this form and a member of our sales team will contact you.


By Alexandra Ames

Microsoft launches a drag-and-drop machine learning tool

Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.

Getting started with machine learning is hard. Even to run the most basic of experiments take a good amount of expertise. All of these new tools great simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.

The new interface for Azure’s automated machine learning tool makes creating a model as easy importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”

For those who want a bit more control from the get-go, Microsoft also today launched a visual interface for its Azure Machine Learning service into preview that will allow developers to build, train and deploy machine learning models without having to touch any code.

This tool, the Azure Machine Learning visual interface looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite that fact that it always seemed like a really useful tool for getting started with machine learning.

Microsoft says that this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and lifecycle management capabilities.

The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.

While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experiences machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.


By Frederic Lardinois

Couchbase’s mobile database gets built-in ML and enhanced synchronization features

Couchbase, the company behind the eponymous NoSQL database, announced a major update to its mobile database today that brings some machine learning smarts, as well as improved synchronization features and enhanced stats and logging support to the software.

“We’ve led the innovation and data management at the edge since the release of our mobile database five years ago,” Couchbase’s VP of Engineering Wayne Carter told me. “And we’re excited that others are doing that now. We feel that it’s very, very important for businesses to be able to utilize these emerging technologies that do sit on the edge to drive their businesses forward, and both making their employees more effective and their customer experience better.”

The latter part is what drove a lot of today’s updates, Carter noted. He also believes that the database is the right place to do some machine learning. So with this release, the company is adding predictive queries to its mobile database. This new API allows mobile apps to take pre-trained machine learning models and run predictive queries against the data that is stored locally. This would allow a retailer to create a tool that can use a phone’s camera to figure out what part a customer is looking for.

To support these predictive queries, Couchbase mobile is also getting support for predictive indexes. “Predictive indexes allow you to create an index on prediction, enabling correlation of real-time predictions with application data in milliseconds,” Carter said. In many ways, that’s also the unique value proposition for bringing machine learning into the database. “What you really need to do is you need to utilize the unique values of a database to be able to deliver the answer to those real-time questions within milliseconds,” explained Carter.

The other major new feature in this release is delta synchronization, which allows businesses to push far smaller updates to the databases on their employees mobile devices. That’s because they only have to receive the information that changed instead of a full updated database. Carter says this was a highly requested feature but until now, the company always had to prioritize work on other components of Couchbase.

This is an especially useful feature for the company’s retail customers, a vertical where it has been quite successful. These users need to keep their catalogs up to data and quite a few of them supply their employees with mobile devices to help shoppers. Rumor has it that Apple, too, is a Couchbase user.

The update also includes a few new features that will be more of interest to operators, including advanced stats reporting and enhanced logging support.

 


By Frederic Lardinois

Blueshift announces $15M Series B to expand AI-fueled cross-channel marketing tool

Blueshift is startup founded by tech industry veterans, who saw first-hand how difficult cross-channel marketing was. They decided to launch a company and build a cross-channel marketing platform from the ground up that uses AI and machine learning to make sense of the growing amount of customer data. Today, the startup announced a $15 million Series B round to keep it going.

The round was led by Softbank Ventures Asia, a fund focused on AI startups like Blueshift . Previous investors Storm Ventures and Nexus Venture Partners also participated. Today’s investment brings the total raised to $30 million, according the company.

Company co-founder and CEO Vijay Chittoor says the marketing landscape is changing, and he believes that requires a new approach to allow marketers to take advantage of the multiple channels where they could be engaging with customers from a single tool.

“If you thought about the world of customer engagement at Walmart or Groupon [or any other retailer] 10 years ago, it was primarily an email problem. Today, we as customers, we’re interacting with these brands on not just email, but also on mobile notifications, Facebook custom audiences and WeChat [and across multiple other channels],” he explained.

He says that this has created a lot more data, which it turns out is a double-edged sword for marketing pros. “I think on one end, it’s exciting for a marketer or a CMO to have more data and more channels. It gives them more ways to connect. But at the same time, it’s also more challenging because now you have to make sense of thousand times more data. And you have to use it intelligently on not just one channel like email, but you’re now trying to make sense of data across 15 different channels,” Chittoor said.

This a crowded field with big players like Adobe, Salesforce and Oracle, among others, offering similar cross-channel, AI-fueled solution. In addition startups are attracting huge chunks of money to attack this problem, including Klayvio pulling in $150 million a couple of weeks ago and Iterable, which landed $50 million last month.

He says his company’s differentiator is the AI piece, and it is this piece that the company’s lead investor in this round has been focusing on in its investments. The company plans to use this round to continue building out its marketing platform and show marketers how to communicate intelligently across channels wherever the consumer happens to be. Customers include LendingTree, Udacity and BBC.


By Ron Miller

The Exit: an AI startup’s McPivot

Five years ago, Dynamic Yield was courting an investment from The New York Times as it looked to shift how publishers paywalled their content. Last month, Chicago-based fast food king McDonald’s bought the Israeli company for $300 million, a source told TechCrunch, with the purpose of rethinking how people order drive-thru chicken nuggets.

The pivot from courting the grey lady to the golden arches isn’t as drastic as it sounds. In a lot of ways, it’s the result of the company learning to say “no” to certain customers. At least, that’s what Bessemer’s Adam Fisher tells us.

The Exit is a new series at TechCrunch. It’s an exit interview of sorts with a VC who was in the right place at the right time but made the right call on an investment that paid off. 

Fisher

Fisher was Dynamic Yield founder Liad Agmon’s first call when he started looking for funds from institutional investors. Bessemer bankrolled the bulk of a $1.7 million funding round which valued the startup at $5 million pre-money back in 2013. The firm ended up putting about $15 million into Dynamic Yield, which raised ~$85 million in total from backers including Marker Capital, Union Tech Ventures, Baidu and The New York Times.

Fisher and I chatted at length about the company’s challenging rise and how Israel’s tech scene is still being underestimated. Fisher has 11 years at Bessemer under his belt and 14 exits including Wix, Intucell, Ravello and Leaba.

The interview has been edited for length and clarity. 


Saying “No”

Lucas Matney: So, right off the bat, how exactly did this tool initially built for publishers end up becoming something that McDonalds wanted?

Adam Fisher: I mean, the story of Dynamic Yield is unique. Liad, the founder and CEO, he was an entrepreneur in residence in our Herzliya office back in 2011. I’d identified him earlier from his previous company, and I just said, ‘Well, that’s the kind of guy I’d love to work with.’ I didn’t like his previous company, but there was something about his charisma, his technology background, his youth, which I just felt like “Wow, he’s going to do something interesting.” And so when he sold his previous company, coincidentally to another Chicago based company called Sears, I invited him and I think he found it very flattering, so he joined us as an EIR.


By Lucas Matney

OpenStack Stein launches with improved Kubernetes support

The OpenStack project, which powers more than 75 public and thousands of private clouds, launched the 19th version of its software this week. You’d think that after 19 updates to the open-source infrastructure platform, there really isn’t all that much new the various project teams could add, given that we’re talking about a rather stable code base here. There are actually a few new features in this release, though, as well as all the usual tweaks and feature improvements you’d expect.

While the hype around OpenStack has died down, we’re still talking about a very active open-source project. On average, there were 155 commits per day during the Stein development cycle. As far as development activity goes, that keeps OpenStack on the same level as the Linux kernel and Chromium.

Unsurprisingly, a lot of that development activity focused on Kubernetes and the tools to manage these container clusters. With this release, the team behind the OpenStack Kubernetes installer brought the launch time for a cluster down from about 10 minutes to five, regardless of the number of nodes. To further enhance Kubernetes support, OpenStack Stein also includes updates to Neutron, the project’s networking service, which now makes it easier to create virtual networking ports in bulk as containers are spun up, and Ironic, the bare-metal provisioning service.

All of that is no surprise, given that according to the project’s latest survey, 61 percent of OpenStack deployments now use both Kubernetes and OpenStack in tandem.

The update also includes a number of new networking features that are mostly targeted at the many telecom users. Indeed, over the course of the last few years, telcos have emerged as some of the most active OpenStack users as these companies are looking to modernize their infrastructure as part of their 5G rollouts.

Besides the expected updates, though, there are also a few new and improved projects here that are worth noting.

“The trend from the last couple of releases has been on scale and stability, which is really focused on operations,” OpenStack Foundation executive director Jonathan Bryce told me. “The new projects — and really most of the new projects from the last year — have all been pretty oriented around real-world use cases.”

The first of these is Placement. “As people build a cloud and start to grow it and it becomes more broadly adopted within the organization, a lot of times, there are other requirements that come into play,” Bryce explained. “One of these things that was pretty simplistic at the beginning was how a request for a resource was actually placed on the underlying infrastructure in the data center.” But as users get more sophisticated, they often want to run specific workloads on machines with certain hardware requirements. These days, that’s often a specific GPU for a machine learning workload, for example. With Placement, that’s a bit easier now.

It’s worth noting that OpenStack had some of this functionality before. The team, however, decided to uncouple it from the existing compute service and turn it into a more generic service that could then also be used more easily beyond the compute stack, turning it more into a kind of resource inventory and tracking tool.

Then, there is also Blazer, a reservation service that offers OpenStack users something akin to AWS Reserved Instances. In a private cloud, the use case for a feature is a bit different, though. But as some of the private clouds got bigger, some users found that they needed to be able to guarantee resources to run some of their regular, overnight batch jobs or data analytics workloads, for example.

As far as resource management goes, it’s also worth highlighting Sahara, which now makes it easier to provision Hadoop clusters on OpenStack.

In previous releases, one of the focus areas for the project was to improve the update experience. OpenStack is obviously a very complex system, so bringing it up to the latest version is also a bit of a complex undertaking. These improvements are now paying off. “Nobody even knows we are running Stein right now,” Vexxhost CEO Mohammed Nasar, who made an early bet on OpenStack for his service, told me. “And I think that’s a good thing. You want to be least impactful, especially when you’re in such a core infrastructure level. […] That’s something the projects are starting to become more and more aware of but it’s also part of the OpenStack software in general becoming much more stable.”

As usual, this release launched only a few weeks before the OpenStack Foundation hosts its bi-annual Summit in Denver. Since the OpenStack Foundation has expanded its scope beyond the OpenStack project, though, this event also focuses on a broader range of topics around open-source infrastructure. It’ll be interesting to see how this will change the dynamics at the event.


By Frederic Lardinois

The right way to do AI in security

Artificial intelligence applied to information security can engender images of a benevolent Skynet, sagely analyzing more data than imaginable and making decisions at lightspeed, saving organizations from devastating attacks. In such a world, humans are barely needed to run security programs, their jobs largely automated out of existence, relegating them to a role as the button-pusher on particularly critical changes proposed by the otherwise omnipotent AI.

Such a vision is still in the realm of science fiction. AI in information security is more like an eager, callow puppy attempting to learn new tricks – minus the disappointment written on their faces when they consistently fail. No one’s job is in danger of being replaced by security AI; if anything, a larger staff is required to ensure security AI stays firmly leashed.

Arguably, AI’s highest use case currently is to add futuristic sheen to traditional security tools, rebranding timeworn approaches as trailblazing sorcery that will revolutionize enterprise cybersecurity as we know it. The current hype cycle for AI appears to be the roaring, ferocious crest at the end of a decade that began with bubbly excitement around the promise of “big data” in information security.

But what lies beneath the marketing gloss and quixotic lust for an AI revolution in security? How did AL ascend to supplant the lustrous zest around machine learning (“ML”) that dominated headlines in recent years? Where is there true potential to enrich information security strategy for the better – and where is it simply an entrancing distraction from more useful goals? And, naturally, how will attackers plot to circumvent security AI to continue their nefarious schemes?

How did AI grow out of this stony rubbish?

The year AI debuted as the “It Girl” in information security was 2017. The year prior, MIT completed their study showing “human-in-the-loop” AI out-performed AI and humans individually in attack detection. Likewise, DARPA conducted the Cyber Grand Challenge, a battle testing AI systems’ offensive and defensive capabilities. Until this point, security AI was imprisoned in the contrived halls of academia and government. Yet, the history of two vendors exhibits how enthusiasm surrounding security AI was driven more by growth marketing than user needs.


By Arman Tabatabai