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Backed by Benchmark, Blue Hexagon just raised $31 million for its deep learning cybersecurity software

February 5, 2019 No Comments

Nayeem Islam spent nearly 11 years with chipmaker Qualcomm, where he founded its Silicon Valley-based R&D facility, recruited its entire team and oversaw research on all aspects of security, including applying machine learning on mobile devices and in the network to detect threats early.

Islam was nothing if not prolific, developing a system for on-device machine learning for malware detection, libraries for optimizing deep learning algorithms on mobile devices, and systems for parallel compute on mobile devices, among other things.

In fact, because of his work, he also saw a big opportunity in better protecting enterprises from cyberthreats through deep neural networks that are capable of processing every raw byte within a file and that can uncover complex relations within datasets. So two years ago, Islam and Saumitra Das, a former Qualcomm engineer with 330 patents to his name and another 450 pending, struck out on their own to create Blue Hexagon, a now 30-person Sunnyvale, Ca.-based company that is today disclosing that it has raised $ 31 million in funding from Benchmark and Altimeter.

The funding comes roughly one year after Benchmark quietly led a $ 6 million Series A round for the firm.

So what has investors so bullish on the company’s prospects, aside from its credentialed founders? In a word, speed, seemingly. According to Islam, Blue Hexagon has created a real-time, cybersecurity platform that he says can detect known and unknown threats at first encounter, then block them in “sub seconds” so the malware doesn’t have time to spread.

The industry has to move to real-time detection, he says, explaining that four new and unique malware samples is released every second, and arguing that traditional security methods can’t keep pace. He says that sandboxes, for example, meaning restricted environments that quarantine cyber threats and keep them from breaching sensitive files, are no longer state of the art. The same is true of signatures, which are mathematical techniques used to validate the authenticity and integrity of a message, software or digital document but are being bypassed by rapidly evolving new malware.

Only time will tell if Blue Hexagon is far more capable of identifying and stopping attackers, as Islam insists is the case. It is not the only startup to apply deep learning to cybersecurity, though it’s certainly one of the first. Critics, some who are protecting their own corporate interests, also worry that hackers can foil security algorithms by targeting the warning flags they look for.

Still, with its technology, its team, and its pitch, Blue Hexagon is starting to persuade not only top investors of its merits, but a growing —  and broad — base of customers, says Islam. “Everyone has this issue, from large banks, insurance companies, state and local governments. Nowhere do you find someone who doesn’t need to be protected.”

Blue Hexagon can even help customers that are already under attack, Islam says, even if it isn’t ideal. “Our goal is to catch an attack as early in the kill chain as possible. But if someone is already being attacked, we’ll see that activity and pinpoint it and be able to turn it off.”

Some damage may already be done, of course. It’s another reason to plan ahead, he says. “With automated attacks, you need automated techniques.” Deep learning, he insists, “is one way of leveling the playing field against attackers.”


Enterprise – TechCrunch


Krisp reduces noise on calls using machine learning, and it’s coming to Windows soon

December 10, 2018 No Comments

If your luck is anything like mine, as soon as you jump on an important call, someone decides it’s a great time to blow some leaves off the sidewalk outside your window. 2Hz’s Krisp is a new desktop app that uses machine learning to subtract background noise like that, or crowds, or even crying kids — while keeping your voice intact. It’s already out for Macs and it’s coming to Windows soon.

I met the creators of Krisp, including 2Hz co-founder Davit Baghdasaryan, earlier this year at UC Berkeley’s Skydeck accelerator, where they demonstrated their then-prototype tech.

The tech involved is complex, but the idea is simple: If you create a machine learning system that understands what the human voice sounds like, on average, then it can listen to an audio signal and select only that part of it, cutting out a great deal of background noise.

Baghdasaryan, formerly of Twilio, originally wanted to create something that would run on mobile networks, so T-Mobile or whoever could tout built-in noise cancellation. This platform approach proved too slow, however, so they decided to go straight to consumers.

“Traction with customers was slow, and this was a problem for a young startup,” Baghdasaryan said in an email later. However, people were loving the idea of ‘muting noise,’ so we decided to switch all our focus and build a user-facing product.”

That was around the time I talked with them in person, incidentally, and just six months later they had released on Mac.

It’s simple: You run the app, and it modifies both the outgoing and incoming audio signals, with the normal noisy signal going in one end and a clean, voice-focused one coming out the other. Everything happens on-device and with very short latency (around 15 milliseconds), so there’s no cloud involved and nothing is ever sent to any server or even stored locally. The team is working on having the software adapt and learn on the fly, but it’s not implemented yet.

Another benefit of this approach is it doesn’t need any special tweaking to work with, say, Skype instead of Webex. Because it works at the level of the OS’s sound processing, whatever app you use just hears the Krisp-modified signal as if it were clean out of your mic.

They launched on Mac because they felt the early-adopter type was more likely to be on Apple’s platform, and the bet seems to have paid off. But a Windows version is coming soon — the exact date isn’t set, but expect it either late this month or early January. (We’ll let you know when it’s live.)

It should be more or less identical to the Mac version, but there will be a special gaming-focused one. Gamers, Baghdasaryan pointed out, are much more likely to have GPUs to run Krisp on, and also have a real need for clear communication (as a PUBG player I can speak to the annoyance of an open mic and clacky keys). So there will likely be a few power-user features specific to gamers, but it’s not set in stone yet.

You may wonder, as I did, why they weren’t going after chip manufacturers, perhaps to include Krisp as a tech built into a phone or computer’s audio processor.

In person, they suggested that this ultimately was also too slow and restrictive. Meanwhile, they saw that there was no real competition in the software space, which is massively easier to enter.

“All current noise cancellation solutions require multiple microphones and a special form factor where the mouth must be close to one of the mics. We have no such requirement,” Baghdasaryan explained. “We can do it with single-mic or operate on an audio stream coming from the network. This makes it possible to run the software in any environment you want (edge or network) and any direction (inbound or outbound).”

If you’re curious about the technical side of things — how it was done with one mic, or at low latency, and so on — there’s a nice explanation Baghdasaryan wrote for the Nvidia blog a little while back.

Furthermore, a proliferation of AI-focused chips that Krisp can run on easily means easy entry to the mobile and embedded space. “We have already successfully ported our DNN to NVIDIA GPUs, Intel CPU/GNA, and ARM. Qualcomm is in the pipeline,” noted Baghdasaryan.

To pursue this work the company has raised a total of $ 2 million so far: $ 500K from Skydeck as well as friends and family for a pre-seed round, then a $ 1.5 M round led by Sierra Ventures and Shanda Group.

Expect the Windows release later this winter, and if you’re already a user, expect a few new features to come your way in the same time scale. You can download Krisp for free here.

Gadgets – TechCrunch


TechSEO Boost: Machine Learning for SEOs

December 4, 2018 No Comments

This year’s TechSEO Boost, an event dedicated to technical SEO and hosted by Catalyst, took place on November 29 in Boston.

Billed as the conference “for developers and advanced SEO specialists,” TechSEO Boost built on the success of the inaugural event in 2017 with a day of enlightening, challenging talks from the sharpest minds in the industry.

Some topics permeated the discourse throughout the day and in particular, machine learning was a recurring theme.

As is the nature of the TechSEO Boost conference, the sessions aimed to go beyond the hype to define what precisely machine learning means for SEO, both today and in future.

The below is a recap of the excellent talk from Britney Muller, Senior SEO Scientist at Moz, entitled (fittingly enough) “Machine Learning for SEOs.”

What is machine learning? A quick recap.

The session opened with a brief primer on the key terms and concepts that fit under the umbrella of “machine learning.”

Muller used the definition in the image below to capture the sense of machine learning as “a subset of AI (Artificial Intelligence) that combines statistics and programming to give computers the ability to “learn” without explicitly being programmed.”

definition what is machine learning

That core idea of “learning” from new stimuli is an important one to grasp as we consider how machine learning can be applied to daily SEO tasks.

Machine learning excels at identifying patterns in huge quantities of data. As such, some of the common examples of machine learning applications today include:

  • Recommender systems (Netflix, Spotify)
  • Ridesharing apps (Uber, Lyft)
  • Digital Assistants (Amazon Alexa, Apple Siri, Google Assistant)

This very ubiquity can make it a challenging concept to grasp, however. In fact, Eric Schmidt at Google has gone so far as to say, “The core thing Google is working on is basically machine learning.”

It is helpful to break this down into the steps that comprise a typical machine learning project, in order to see how we might apply this to everyday SEO tasks.

The machine learning process

The image below represents the machine learning process Muller shared at TechSEO Boost:

the machine learning process

It is important to bear in mind that some of the training data should be reserved for testing at a later point in the process.

Where possible, this data should also be labelled clearly to help the machine learning algorithm identify classifications and categories within a noisy data set.

It is for precisely this reason that Google asks us to label images to verify our identity:

street signs Google images

This demonstrates our human ability to pick out objects in cluttered contexts, but it has the added benefit of providing Google with higher quality image data.

The pitfalls of an unsupervised approach to machine learning, and a training data set that is open to interpretation, were laid bare just last week.

Google’s ‘Smart Compose’ feature within Gmail has demonstrated gender bias by preferring certain pronouns when predicting what a user might want to say.

As reported in Reuters, “Gmail product manager Paul Lambert said a company research scientist discovered the problem in January when he typed “I am meeting an investor next week,” and Smart Compose suggested a possible follow-up question: “Do you want to meet him?” instead of “her.”

gmail smart compose

The challenge here is not restricted to projects on such a scale. Marketers who want to get their hands dirty must be aware of the limitations of machine learning, as well as its exciting possibilities.

Muller added that people tend to overfit their data, which reduces the accuracy and flexibility of the model they are using. This (very common) phenomenon occurs when a model corresponds very closely with one specific data set, reducing its applicability to new scenarios.

The ability to scale effectively is what gives machine learning its appeal, so overfitting is something to be avoided with care. There is a good primer to this topic here and it is also explained very well through this image:

the best way to explain overfitting

So, how exactly can this subset of AI be used to improve SEO performance?

How you can use machine learning for SEO

As is the case with all hype-friendly technologies, businesses are keen to get involved with machine learning. However, the point is not to “use machine learning” through fear of being left behind, but rather to find the best uses of machine learning for each business.

Britney Muller shared some examples from her role at Moz during her session at TechSEO Boost.

The first was an approach to automated meta description generation using the Algorithmia Advanced Content Summarizer, which was then compared to Google’s approach to automated descriptions pulled directly from the landing page.

Meta descriptions remain an important asset when trying to encourage a positive click-through rate, but a lot of time is spent crafting these snippets. An automated alternative that can interpret the meaning of landing pages and create clickable summaries for display in the SERPs would be very useful.

serp q&a results comparison

Muller shared some examples, such as the image above, to demonstrate the comparison between the two approaches. The machine learning approach is not perfect and may require some tweaking, but it does an excellent job of conveying the page’s intent when compared to Google’s selection.

The team at Moz has since built this into Google Sheets:

google sheets meta descriptions

Although this is not a product other businesses can access right now, an alternative way of achieving automated meta descriptions has been shared by Paul Shapiro (the TechSEO Boost host) via Github here.

Automated image optimization

Another fascinating use of machine learning for SEO is the automation of image optimization. Britney Muller showed how, in under 20 minutes, it is possible to train an algorithm to distinguish between cats and ducks, then use this model on a new data set with a high level of accuracy.

recognize ducks vs snakes

For large retailers, the application of this method could be very beneficial. With so many new images added to the inventory every day, and with visual search on the rise, a scalable image labeling system would prove very profitable. As demonstrated at TechSEO Boost, this is now a very realistic possibility for businesses willing to build their own model.

A further use of machine learning described by Britney Muller was the transcription of podcasts. An automated approach to this task can turn audio files into something much more legible for a search engine, thereby helping with indexation and ranking for relevant topics.

Muller detailed an approach using the Amazon Transcribe product through Amazon Web Services to achieve this aim.

amazon transcribe tool

The audio is broken down and delivered in a J-SON file in a lot of detail, with the different speakers on the podcast labelled separately.

transcription json string

There was not enough time in the session to work through every potential use of machine learning for SEO, but Muller’s core message was that everyone in the industry should be working towards at least a working knowledge of these concepts.

Some further opportunities for experimentation were listed as follows:

opportunities with machine learning

As we can see, machine learning truly excels when working with large data sets to identify patterns.

Tools and resources

The best way to get engaged is to combine theory with practice. This is almost always the case, but it is a particularly valid piece of advice in relation to programming.

Muller’s was not the first or last talk to reference Google Codelabs throughout the day.

google codelabs

There are more resources out there than ever before and the likes of Amazon and Google want machine learning to be approachable. Amazon has launched a machine learning course and Google’s crash course is a fantastic way to learn the components of a successful project.

google codelabs machine learning crash course

The Google-owned Kaggle is always a great place to trial new data sets and review the innovative work performed by data scientists around the world, once a basic grasp has been attained.

Furthermore, Google’s Colaboratory makes it easy to get started on a project and work with a remote team.

Key takeaways: machine learning for SEOs

What became particularly clear through Muller’s talk is how approachable machine learning applications can be for SEOs. Moreover, the room for experimentation is unprecedented, for those willing to invest some time in the discipline.

key takeaways

The post TechSEO Boost: Machine Learning for SEOs appeared first on Search Engine Watch.

Search Engine Watch


Not Just Another AI and Machine Learning Discussion [Submit Your Questions to the Pros!]

November 30, 2018 No Comments

Time to talk through what you ACTUALLY want to know about AI and machine learning. Submit your questions!

Read more at PPCHero.com
PPC Hero


New Uber feature uses machine learning to sort business and personal rides

August 14, 2018 No Comments

Uber announced a new program today called Profile Recommendations that takes advantage of machine intelligence to reduce user error when switching between personal and business accounts.

It’s not unusual for a person to have both types of accounts. When you’re out and about, it’s easy to forget to switch between them when appropriate. Uber wants to help by recommending the correct one.

“Using machine learning, Uber can predict which profile and corresponding payment method an employee should be using, and make the appropriate recommendation,” Ronnie Gurion, GM and Global Head of Uber for Business wrote in a blog post announcing the new feature.

Uber has been analyzing a dizzying amount of trip data for so long, it can now (mostly) understand the purpose of a given trip based on the details of your request. While it’s certainly not perfect because it’s not always obvious what the purpose is, Uber believes it can determine the correct intention 80 percent of the time. For that remaining 20 percent, when it doesn’t get it right, Uber is hoping to simplify corrections too.

Photo: Uber

Business users can now also assign trip reviewers — managers or other employees who understand the employee’s usage patterns, and can flag questionable rides. Instead of starting an email thread or complicated bureaucratic process to resolve an issue, the employee can now see these flagged rides and resolve them right in the app. “This new feature not only saves the employee’s and administrator’s time, but it also cuts down on delays associated with closing out reports,” Gurion wrote in the blog post announcement.

Uber also announced that it’s supporting a slew of new expense reporting software to simplify integration with these systems. They currently have integrations with Certify, Chrome River, Concur and Expensify. They will be adding support for Expensya, Happay, Rydoo, Zeno by Serko and Zoho Expense starting in September.

All of this should help business account holders deal with Uber expenses more efficiently, while integrating with many of the leading expense programs to move data smoothly from Uber to a company’s regular record-keeping systems.


Enterprise – TechCrunch


Putting machine learning into the hands of every advertiser

July 31, 2018 No Comments


This post originally appeared on the Inside AdWords blog

The ways people get things done are constantly changing, from finding the closest coffee shop to organizing family photos. Earlier this year, we explored how machine learning is being used to improve our consumer products and help people get stuff done.

In just one hour, we’ll share how we’re helping marketers unlock more opportunities for their businesses with our largest deployment of machine learning in ads. We’ll explore how this technology works in our products and why it’s key to delivering the helpful and frictionless experiences consumers expect from brands.

Join us live today at 9am PT (12pm ET).

Deliver more relevance with responsive search ads

Consumers today are more curious, more demanding, and they expect to get things done faster because of mobile. As a result, they expect your ads to be helpful and personalized. Doing this isn’t easy, especially at scale. That’s why we’re introducing responsive search ads. Responsive search ads combine your creativity with the power of Google’s machine learning to help you deliver relevant, valuable ads.

Simply provide up to 15 headlines and 4 description lines, and Google will do the rest. By testing different combinations, Google learns which ad creative performs best for any search query. So people searching for the same thing might see different ads based on context.

We know this kind of optimization works: on average, advertisers who use Google’s machine learning to test multiple creative see up to 15 percent more clicks.1 Responsive search ads will start rolling out to advertisers over the next several months.

Maximize relevance and performance on YouTube

People watch over 1 billion hours of video on YouTube every day. And increasingly, they’re tuning in for inspiration and information on purchases large and small. For example, nearly 1 in 2 car buyers say they turn to YouTube for information before their purchase.2 And nearly 1 in 2 millennials go there for food preparation tips before deciding what ingredients to buy.3 That means it’s critical your video ads show at the right moment to the right audience.

Machine learning helps us turn that attention into results on YouTube. In the past, we’ve helped you optimize campaigns for views and impressions. Later this year, we’re rolling out Maximize lift to help you reach people who are most likely to consider your brand after seeing a video ad. This new Smart Bidding strategy is also powered by machine learning. It automatically adjusts bids at auction time to maximize the impact your video ads have on brand perception throughout the consumer journey.

Maximize lift is available now as a beta and will roll out to advertisers globally later this year.

Drive more foot traffic with Local campaigns

Whether they start their research on YouTube or Google, people still make the majority of their purchases in physical stores. In fact, mobile searches for “near me” have grown over 3X in the past two years4, and almost 80 percent of shoppers will go in store when there’s an item they want immediately.5 For many of you, that means driving foot traffic to your brick-and-mortar locations is critical—especially during key moments in the year, like in-store events or promotions.

Today we’re introducing Local campaigns: a new campaign type designed to drive store visits exclusively. Provide a few simple things—like your business locations and ad creative—and Google automatically optimizes your ads across properties to bring more customers into your store.

Show your business locations across Google properties and networks

Local campaigns will roll out to advertisers globally over the coming months.

Get the most from your Shopping campaigns

Earlier this year, we rolled out a new Shopping campaign type that optimizes performance based on your goals. These Smart Shopping campaign help you hit your revenue goals without the need to manually manage and bid to individual products. In the coming months, we’re improving them to optimize across multiple business goals.

Beyond maximize conversion value, you’ll also be able to select store visits or new customers as goals. Machine learning factors in the likelihood that a click will result in any of these outcomes and helps adjust bids accordingly.

Machine learning is also used to optimize where your Shopping ads show—on Google.com, Image Search, YouTube and millions of sites and apps across the web—and which products are featured. It takes into account a wide range of signals, like seasonal demand and pricing. Brands like GittiGidiyor, an eBay company, are using Smart Shopping campaigns to simplify how they manage their ads and deliver better results. GittiGidiyor was able to increase return on ad spend by 28 percent and drive 4 percent more sales, while saving time managing campaigns.

We’re also adding support for leading e-commerce platforms to help simplify campaign management. In the coming weeks, you’ll be able to set up and manage Smart Shopping campaigns right from Shopify, in addition to Google Ads.

Tune in to see more

This is an important moment for marketers and we’re excited to be on this journey with you. Tune in at 9am PT (12pm ET) today to see it all unfold at Google Marketing Live.

For the latest news, follow the new Google Ads blog. And check out g.co/adsannouncements for more information about product updates and announcements.

1 Internal Google data.
2 Google / Kantar TNS, Auto CB Gearshift Study, US, 2017. n=312 new car buyers who watched online video.
3 Google / Ipsos, US, November 2017.
4 Internal Google data, U.S., July–Dec. 2015 vs. July–Dec. 2017.
5 Google/Ipsos, U.S., “Shopping Tracker,” Online survey, n=3,613 online Americans 13+ who shopped in the past two days, Oct.–Dec. 2017.


Google Analytics Blog


Learning to Rank

July 17, 2018 No Comments

My last Post was Five Years of Google Ranking Signals, and I start that post by saying that there are other posts about ranking signals that have some issues. But, I don’t want to turn people away from looking at one recent post that did contain a lot of useful information.

Cyrus Shepard recently published a post about Google Sucess Factors on Zyppy.com which I would recommend that you also check out.

Cyrus did a video with Ross Hudgins on Seige Media where he talked about those Ranking signals with Cyrus, called Google Ranking Factors with Cyrus Shepard. I’m keeping this post short on purpose, to make the discussion about ranking the focus of this post, and the star. There is some really good information in the Video and in the post from Cyrus. Cyrus takes a different approach on writing about ranking signals from what I wrote, but it’s worth the time visiting and listening and watching.

And have fun learning to rank.


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The post Learning to Rank appeared first on SEO by the Sea ⚓.


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Workday acquires Rallyteam to fuel machine learning efforts

June 11, 2018 No Comments

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.


Startups – TechCrunch


Workday acquires Rallyteam to fuel machine learning efforts

June 9, 2018 No Comments

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.


Enterprise – TechCrunch


Google Kubeflow, machine learning for Kubernetes, begins to take shape

May 5, 2018 No Comments

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.


Enterprise – TechCrunch