Back at its re:Invent conference in November, AWS announced its $249 DeepLens, a camera that’s specifically geared toward developers who want to build and prototype vision-centric machine learning models. The company started taking pre-orders for DeepLens a few months ago, but now the camera is actually shipping to developers.
Ahead of today’s launch, I had a chance to attend a workshop in Seattle with DeepLens senior product manager Jyothi Nookula and Amazon’s VP for AI Swami Sivasubramanian to get some hands-on time with the hardware and the software services that make it tick.
DeepLens is essentially a small Ubuntu- and Intel Atom-based computer with a built-in camera that’s powerful enough to easily run and evaluate visual machine learning models. In total, DeepLens offers about 106 GFLOPS of performance.
The hardware has all of the usual I/O ports (think Micro HDMI, USB 2.0, Audio out, etc.) to let you create prototype applications, no matter whether those are simple toy apps that send you an alert when the camera detects a bear in your backyard or an industrial application that keeps an eye on a conveyor belt in your factory. The 4 megapixel camera isn’t going to win any prizes, but it’s perfectly adequate for most use cases. Unsurprisingly, DeepLens is deeply integrated with the rest of AWS’s services. Those include the AWS IoT service Greengrass, which you use to deploy models to DeepLens, for example, but also SageMaker, Amazon’s newest tool for building machine learning models.
These integrations are also what makes getting started with the camera pretty easy. Indeed, if all you want to do is run one of the pre-built samples that AWS provides, it shouldn’t take you more than 10 minutes to set up your DeepLens and deploy one of these models to the camera. Those project templates include an object detection model that can distinguish between 20 objects (though it had some issues with toy dogs, as you can see in the image above), a style transfer example to render the camera image in the style of van Gogh, a face detection model and a model that can distinguish between cats and dogs and one that can recognize about 30 different actions (like playing guitar, for example). The DeepLens team is also adding a model for tracking head poses. Oh, and there’s also a hot dog detection model.
But that’s obviously just the beginning. As the DeepLens team stressed during our workshop, even developers who have never worked with machine learning can take the existing templates and easily extend them. In part, that’s due to the fact that a DeepLens project consists of two parts: the model and a Lambda function that runs instances of the model and lets you perform actions based on the model’s output. And with SageMaker, AWS now offers a tool that also makes it easy to build models without having to manage the underlying infrastructure.
You could do a lot of the development on the DeepLens hardware itself, given that it is essentially a small computer, though you’re probably better off using a more powerful machine and then deploying to DeepLens using the AWS Console. If you really wanted to, you could use DeepLens as a low-powered desktop machine as it comes with Ubuntu 16.04 pre-installed.
For developers who know their way around machine learning frameworks, DeepLens makes it easy to import models from virtually all the popular tools, including Caffe, TensorFlow, MXNet and others. It’s worth noting that the AWS team also built a model optimizer for MXNet models that allows them to run more efficiently on the DeepLens device.
So why did AWS build DeepLens? “The whole rationale behind DeepLens came from a simple question that we asked ourselves: How do we put machine learning in the hands of every developer,” Sivasubramanian said. “To that end, we brainstormed a number of ideas and the most promising idea was actually that developers love to build solutions as hands-on fashion on devices.” And why did AWS decide to build its own hardware instead of simply working with a partner? “We had a specific customer experience in mind and wanted to make sure that the end-to-end experience is really easy,” he said. “So instead of telling somebody to go download this toolkit and then go buy this toolkit from Amazon and then wire all of these together. […] So you have to do like 20 different things, which typically takes two or three days and then you have to put the entire infrastructure together. It takes too long for somebody who’s excited about learning deep learning and building something fun.”
So if you want to get started with deep learning and build some hands-on projects, DeepLens is now available on Amazon. At $249, it’s not cheap, but if you are already using AWS — and maybe even use Lambda already — it’s probably the easiest way to get started with building these kind of machine learning-powered applications.
News Source = techcrunch.com
Oracle could be feeling cloud transition growing pains
Oracle is learning that it’s hard for enterprise companies born in the data center to make the transition to the cloud, an entirely new way of doing business. Yesterday it reported its earnings and it was a mixed bag, made harder by changing the way the company counts cloud revenue.
In its earnings press release from yesterday, it put it this way: “Q4 Cloud Services and License Support revenues were up 8% to $6.8 billion. Q4 Cloud License and On-Premise License revenues were down 5% to $2.5 billion.”
Let’s compare that with the language from their Q3 revenue in March: “Cloud Software as a
Service (SaaS) revenues were up 33% to $1.2 billion. Cloud Platform as a Service (PaaS) plus Infrastructure as a Service (IaaS) revenues were up 28% to $415 million. Total Cloud Revenues were up 32% to $1.6 billion.”
See how they broke out the cloud revenue loudly and proudly in March, yet chose to combine their cloud revenue with license revenue in June.
In the post-reporting earnings call, Safra Catz, Oracle Co-CEO, responding to a question from analyst John DiFucci, took exception to the idea that the company was somehow obfuscating cloud revenue by reporting it in this way. “So first of all, there is no hiding. I told you the Cloud number, $1.7 billion. You can do the math. You see we are right where we said we’d be.”
She says the new reporting method is due to the new combined licensing products that lets customer use their license on-premises or in the cloud. Fair enough, but if your business is booming you probably want to let investors know about that. They seem to be uneasy about this approach with the stock down over 7 percent today as of publishing this article.
Oracle could of course settle all of this by spelling out their cloud revenue, but instead chose a different path. John Dinsdale, an analyst with Synergy Research, a firm that watches the cloud market was dubious about Oracle’s reasoning.
“Generally speaking, when a company chooses to reduce the amount of financial detail it shares on its key strategic initiatives, that is not a good sign. I think one of the justifications put forward is that is becoming difficult to differentiate between cloud and non-cloud revenues. If that is indeed what Oracle is claiming, I have a hard time buying into that argument. Its competitors are all moving in the opposite direction,” he said.
Indeed most are. While it’s often hard to tell exactly the nature of cloud revenue, the bigger players have been more open about this. For instance in its most recent earnings report, Microsoft reported its Azure cloud revenue grew 93 percent. Amazon reported its cloud revenue from AWS was up 49 percent to $5.4 billion in revenue, getting very specific about the revenue number.
Further you can see from Synergy’s most recent market share cloud growth numbers from the 4th quarter last year, Oracle was lumped in with “the Next 10,” not large enough to register on its own.
That Oracle chose not to break out cloud revenue this quarter can’t be seen as a good sign. To be fair, we haven’t really seen Google break out their cloud revenue either with one exception in February. But when the guys at the top of the market shout about their growth, and the guys further down don’t, you can draw your own conclusions.
News Source = techcrunch.com
After twenty years of Salesforce, what Marc Benioff got right and wrong about the cloud
As we enter the 20th year of Salesforce, there’s an interesting opportunity to reflect back on the change that Marc Benioff created with the software-as-a-service (SaaS) model for enterprise software with his launch of Salesforce.com.
This model has been validated by the annual revenue stream of SaaS companies, which is fast approaching $100 billion by most estimates, and it will likely continue to transform many slower-moving industries for years to come.
However, for the cornerstone market in IT — large enterprise-software deals — SaaS represents less than 25 percent of total revenue, according to most market estimates. This split is even evident in the most recent high profile “SaaS” acquisition of GitHub by Microsoft, with over 50 percent of GitHub’s revenue coming from the sale of their on-prem offering, GitHub Enterprise.
Data privacy and security is also becoming a major issue, with Benioff himself even pushing for a U.S. privacy law on par with GDPR in the European Union. While consumer data is often the focus of such discussions, it’s worth remembering that SaaS providers store and process an incredible amount of personal data on behalf of their customers, and the content of that data goes well beyond email addresses for sales leads.
It’s time to reconsider the SaaS model in a modern context, integrating developments of the last nearly two decades so that enterprise software can reach its full potential. More specifically, we need to consider the impact of IaaS and “cloud-native computing” on enterprise software, and how they’re blurring the lines between SaaS and on-premises applications. As the world around enterprise software shifts and the tools for building it advance, do we really need such stark distinctions about what can run where?
The original cloud software thesis
In his book, Behind the Cloud, Benioff lays out four primary reasons for the introduction of the cloud-based SaaS model:
- Realigning vendor success with customer success by creating a subscription-based pricing model that grows with each customer’s usage (providing the opportunity to “land and expand”). Previously, software licenses often cost millions of dollars and were paid upfront, each year after which the customer was obligated to pay an additional 20 percent for support fees. This traditional pricing structure created significant financial barriers to adoption and made procurement painful and elongated.
- Putting software in the browser to kill the client-server enterprise software delivery experience. Benioff recognized that consumers were increasingly comfortable using websites to accomplish complex tasks. By utilizing the browser, Salesforce avoided the complex local client installation and allowed its software to be accessed anywhere, anytime and on any device.
- Sharing the cost of expensive compute resources across multiple customers by leveraging a multi-tenant architecture. This ensured that no individual customer needed to invest in expensive computing hardware required to run a given monolithic application. For context, in 1999 a gigabyte of RAM cost about $1,000 and a TB of disk storage was $30,000. Benioff cited a typical enterprise hardware purchase of $385,000 in order to run Siebel’s CRM product that might serve 200 end-users.
- Democratizing the availability of software by removing the installation, maintenance and upgrade challenges. Drawing from his background at Oracle, he cited experiences where it took 6-18 months to complete the installation process. Additionally, upgrades were notorious for their complexity and caused significant downtime for customers. Managing enterprise applications was a very manual process, generally with each IT org becoming the ops team executing a physical run-book for each application they purchased.
These arguments also happen to be, more or less, that same ones made by infrastructure-as-a-service (IaaS) providers such as Amazon Web Services during their early days in the mid-late ‘00s. However, IaaS adds value at a layer deeper than SaaS, providing the raw building blocks rather than the end product. The result of their success in renting cloud computing, storage and network capacity has been many more SaaS applications than ever would have been possible if everybody had to follow the model Salesforce did several years earlier.
Suddenly able to access computing resources by the hour—and free from large upfront capital investments or having to manage complex customer installations—startups forsook software for SaaS in the name of economics, simplicity and much faster user growth.
It’s a different IT world in 2018
Fast-forward to today, and in some ways it’s clear just how prescient Benioff was in pushing the world toward SaaS. Of the four reasons laid out above, Benioff nailed the first two:
- Subscription is the right pricing model: The subscription pricing model for software has proven to be the most effective way to create customer and vendor success. Years ago already, stalwart products like Microsoft Office and the Adobe Suite successfully made the switch from the upfront model to thriving subscription businesses. Today, subscription pricing is the norm for many flavors of software and services.
- Better user experience matters: Software accessed through the browser or thin, native mobile apps (leveraging the same APIs and delivered seamlessly through app stores) have long since become ubiquitous. The consumerization of IT was a real trend, and it has driven the habits from our personal lives into our business lives.
In other areas, however, things today look very different than they did back in 1999. In particular, Benioff’s other two primary reasons for embracing SaaS no longer seem so compelling. Ironically, IaaS economies of scale (especially once Google and Microsoft began competing with AWS in earnest) and software-development practices developed inside those “web scale” companies played major roles in spurring these changes:
- Computing is now cheap: The cost of compute and storage have been driven down so dramatically that there are limited cost savings in shared resources. Today, a gigabyte of RAM is about $5 and a terabyte of disk storage is about $30 if you buy them directly. Cloud providers give away resources to small users and charge only pennies per hour for standard-sized instances. By comparison, at the same time that Salesforce was founded, Google was running on its first data center—with combined total compute and RAM comparable to that of a single iPhone X. That is not a joke.
- Installing software is now much easier: The process of installing and upgrading modern software has become automated with the emergence of continuous integration and deployment (CI/CD) and configuration-management tools. With the rapid adoption of containers and microservices, cloud-native infrastructure has become the de facto standard for local development and is becoming the standard for far more reliable, resilient and scalable cloud deployment. Enterprise software packed as a set of Docker containers orchestrated by Kubernetes or Docker Swarm, for example, can be installed pretty much anywhere and be live in minutes.
What Benioff didn’t foresee
Several other factors have also emerged in the last few years that beg the question of whether the traditional definition of SaaS can really be the only one going forward. Here, too, there’s irony in the fact that many of the forces pushing software back toward self-hosting and management can be traced directly to the success of SaaS itself, and cloud computing in general:
- Cloud computing can now be “private”: Virtual private clouds (VPCs) in the IaaS world allow enterprises to maintain root control of the OS, while outsourcing the physical management of machines to providers like Google, DigitalOcean, Microsoft, Packet or AWS. This allows enterprises (like Capital One) to relinquish hardware management and the headache it often entails, but retain control over networks, software and data. It is also far easier for enterprises to get the necessary assurance for the security posture of Amazon, Microsoft and Google than it is to get the same level of assurance for each of the tens of thousands of possible SaaS vendors in the world.
- Regulations can penalize centralized services: One of the underappreciated consequences of Edward Snowden’s leaks, as well as an awakening to the sometimes questionable data-privacy practices of companies like Facebook, is an uptick in governments and enterprises trying to protect themselves and their citizens from prying eyes. Using applications hosted in another country or managed by a third party exposes enterprises to a litany of legal issues. The European Union’s GDPR law, for example, exposes SaaS companies to more potential liability with each piece of EU-citizen data they store, and puts enterprises on the hook for how their SaaS providers manage data.
- Data breach exposure is higher than ever: A corollary to the point above is the increased exposure to cybercrime that companies face as they build out their SaaS footprints. All it takes is one employee at a SaaS provider clicking on the wrong link or installing the wrong Chrome extension to expose that provider’s customers’ data to criminals. If the average large enterprise uses 1,000+ SaaS applications and each of those vendors averages 250 employees, that’s an additional 250,000 possible points of entry for an attacker.
- Applications are much more portable: The SaaS revolution has resulted in software vendors developing their applications to be cloud-first, but they’re now building those applications using technologies (such as containers) that can help replicate the deployment of those applications onto any infrastructure. This shift to what’s called cloud-native computing means that the same complex applications you can sign up to use in a multi-tenant cloud environment can also be deployed into a private data center or VPC much easier than previously possible. Companies like BigID, StackRox, Dashbase and others are taking a private cloud-native instance first approach to their application offerings. Meanwhile SaaS stalwarts like Atlassian, Box, Github and many others are transitioning over to Kubernetes driven, cloud-native architectures that provide this optionality in the future.
- The script got flipped on CIOs: Individuals and small teams within large companies now drive software adoption by selecting the tools (e.g., GitHub, Slack, HipChat, Dropbox), often SaaS, that best meet their needs. Once they learn what’s being used and how it’s working, CIOs are faced with the decision to either restrict network access to shadow IT or pursue an enterprise license—or the nearest thing to one—for those services. This trend has been so impactful that it spawned an entirely new category called cloud access security brokers—another vendor that needs to be paid, an additional layer of complexity, and another avenue for potential problems. Managing local versions of these applications brings control back to the CIO and CISO.
The future of software is location agnostic
As the pace of technological disruption picks up, the previous generation of SaaS companies is facing a future similar to the legacy software providers they once displaced. From mainframes up through cloud-native (and even serverless) computing, the goal for CIOs has always been to strike the right balance between cost, capabilities, control and flexibility. Cloud-native computing, which encompasses a wide variety of IT facets and often emphasizes open source software, is poised to deliver on these benefits in a manner that can adapt to new trends as they emerge.
The problem for many of today’s largest SaaS vendors is that they were founded and scaled out during the pre-cloud-native era, meaning they’re burdened by some serious technical and cultural debt. If they fail to make the necessary transition, they’ll be disrupted by a new generation of SaaS companies (and possibly traditional software vendors) that are agnostic toward where their applications are deployed and who applies the pre-built automation that simplifies management. This next generation of vendors will more control in the hands of end customers (who crave control), while maintaining what vendors have come to love about cloud-native development and cloud-based resources.
So, yes, Marc Benioff and Salesforce were absolutely right to champion the “No Software” movement over the past two decades, because the model of enterprise software they targeted needed to be destroyed. In the process, however, Salesforce helped spur a cloud computing movement that would eventually rewrite the rules on enterprise IT and, now, SaaS itself.
News Source = techcrunch.com
India’s Locus raises $4M to expand its logistics management service worldwide
Locus, a three-year-old startup that helps companies map out their logistics, has pulled in $4 million in funding to grow its global footprint outside of its native India.
The round is described as pre-Series B and it was provided by Rocketship.vc, Recruit Strategic Partners, pi Ventures and DSP Group’s Hemendra Kothari. Existing backers Blume Ventures, Exfinity Venture Partners, BeeNext and growX ventures also took part. Bengaluru-based Locus previously raised a $2.75 million Series A in 2016.
The company was founded in 2015 by Nishith Rastogi and Geet Garg, two ex Amazon engineers who met when working on machine learning for AWS. Initially the duo developed a safety app that mapped out optimal routes to let a ride-hailing customer sense if their driver was going rogue and not sticking to the designated trip, but it later pivoted into logistics tracking after feedback from enterprise users.
Today, Locus is focused on helping customers optimize the operational side of their logistics, whether that is moving people, goods or more at scale. It doesn’t cover ride-hailing and it isn’t necessarily focused on ensuring the faster route. Instead, it tackles complex challenges such as helping FCMGs optimize travel for their management — the key focus being on spending as much time in stores for meetings — or helping organizations move large orders by figuring out how many trucks are needed, which routes are optimal, etc.
Co-founder and CEO Rastogi described the role as that of “chief supply chain officer.”
“We want to automate all human decisions around logistics,” he explained to TechCrunch in an interview, adding that the business makes use of machine learning and artificial intelligence to suss out routes and operational approaches.
He added that the machine-based approach can trump human logic in some cases, thanks to the sheer amounts of data it is based on. In one example, he explained how the Locus system had advised trucks taking a long haul trip to return to the client HQ using a different route. Initially the team figured there was a problem, but on closer inspection they found that the return route cut out a steep hill which, while fine to travel down on the outbound led, was best avoided on the return trip.
That decision, Rastogi said, was based on travel data that the system had observed and might not ordinarily have been made by human-based analysts. To help with the system, the company also provides a $500-priced scanner — “SizeUp,” pictured below — for measuring packages. The idea is to not only make the tech portable but affordable enough that it can be used companies of all sizes.
The company began to expand to overseas markets this year with moves into North America — both Canada and the U.S. — and Southeast Asia. Rastogi said the new capital will go towards expanding its presence in those markets. Later this year, he said, Locus plans to raise a “significant” Series B round, among the objectives for that is a dedicated technical team in Tel Aviv to complement the work happening in India.
News Source = techcrunch.com
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