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Microsoft has acquired GitHub for $7.5B in stock

2018-06-04 - By 

After a week of rumors, Microsoft  today confirmed that it has acquired GitHubthe popular Git-based code sharing and collaboration service. The price of the acquisition was $7.5 billion in Microsoft stock. GitHub raised $350 million and we know that the company was valued at about $2 billion in 2015.

Former Xamarin CEO Nat Friedman (and now Microsoft corporate vice president) will become GitHub’s CEO. GitHub founder and former CEO Chris Wanstrath will become a Microsoft technical fellow and work on strategic software initiatives. Wanstrath had retaken his CEO role after his co-founder Tom Preston-Werner resigned following a harassment investigation in 2014.

The fact that Microsoft is installing a new CEO for GitHub is a clear sign that the company’s approach to integrating GitHub will be similar to hit it is working with LinkedIn. “GitHub will retain its developer-first ethos and will operate independently to provide an open platform for all developers in all industries,” a Microsoft spokesperson told us.

GitHub says that as of March 2018, there were 28 million developers in its community, and 85 million code repositories, making it the largest host of source code globally and a cornerstone of how many in the tech world build software.

But despite its popularity with enterprise users, individual developers and open source projects, GitHub has never turned a profit and chances are that the company decided that an acquisition was preferable over trying to IPO.

GitHub’s main revenue source today is paid accounts, which allows for private repositories and a number of other features that enterprises need, with pricing ranging from $7 per user per month to $21/user/month. Those building public and open source projects can use it for free.

While numerous large enterprises use GitHub as their code sharing service of choice, it also faces quite a bit of competition in this space thanks to products like GitLab and Atlassian’s Bitbucket, as well as a wide range of other enterprise-centric code hosting tools.

Microsoft is acquiring GitHub because it’s a perfect fit for its own ambitions to be the go-to platform for every developer, and every developer need, no matter the platform.

Microsoft has long embraced the Git protocol and is using it in its current Visual Studio Team Services product, which itself used to compete with GitHub’s enterprise service. Knowing GitHub’s position with developers, Microsoft has also leaned on the service quite a bit itself, too and some in the company already claim it is the biggest contributor to GitHub today.

Yet while Microsoft’s stance toward open source has changed over the last few years, many open source developers will keep a very close look at what the company will do with GitHub after the acquisition. That’s because there is a lot of distrust of Microsoft in this cohort, which is understandable given Microsoft’s history.

In fact, TechCrunch received a tip on Friday, which noted not only that the deal had already closed, but that open source software maintainers were already eyeing up alternatives and looking potentially to abandon GitHub in the wake of the deal. Some developers (not just those working in open source) were not wasting timeeven to wait for a confirmation of the deal before migrating.

While GitHub is home to more than just open source software, if such a migration came to pass, it would be a very bad look both for GitHub and Microsoft. And, it would a particularly ironic turn, given the very origins of Git: the versioning control system was created by Linus Torvalds in 2005 when he was working on development of the Linux kernel, in part as a response to a previous system, BitKeeper, changing its terms away from being free to use.

The new Microsoft under CEO Satya Nadella strikes us as a very different company from the Microsoft of ten years ago — especially given that the new Microsoft has embraced open source — but it’s hard to forget its earlier history of trying to suppress Linux.

“Microsoft is a developer-first company, and by joining forces with GitHub we strengthen our commitment to developer freedom, openness and innovation,” said Nadella in today’s announcement. “We recognize the community responsibility we take on with this agreement and will do our best work to empower every developer to build, innovate and solve the world’s most pressing challenges.”

Yet at the same time, it’s worth remembering that Microsoft is now a member of the Linux Foundation and regularly backs a number of open source projects. And Windows now has the Linux subsystem while VS Code, the company’s free code editing tool is open source and available on GitHub, as are .NET Core and numerous other Microsoft-led projects.

And many in the company were defending Microsoft’s commitment to GitHub and its principles, even before the deal was announced.

Still, you can’t help but wonder how Microsoft might leverage GitHub within its wider business strategy, which could see the company build stronger bridges between GitHub and Azure, its cloud hosting service, and its wide array of software and collaboration products. Microsoft is no stranger to ingesting huge companies. One of them, LinkedIn, might be another area where Microsoft might explore synergies, specifically around areas like recruitment and online tutorials and education.

 

Original article here.

 


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State Of Machine Learning And AI, 2017

2017-10-01 - By 

AI is receiving major R&D investment from tech giants including Google, Baidu, Facebook and Microsoft.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled   How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies’ prospects for further deployment, and AI’s impact on markets, governments, and individuals.  McKinsey Analytics was also utilized in the development of this study and discussion paper.

Key takeaways from the study include the following:

  • Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. The current rate of AI investment is 3X the external investment growth since 2013. McKinsey found that 20% of AI-aware firms are early adopters, concentrated in the high-tech/telecom, automotive/assembly and financial services industries. The graphic below illustrates the trends the study team found during their analysis.
  • AI is turning into a race for patents and intellectual property (IP) among the world’s leading tech companies. McKinsey found that only a small percentage (up to 9%) of Venture Capital (VC), Private Equity (PE), and other external funding. Of all categories that have publically available data, M&A grew the fastest between 2013 And 2016 (85%).The report cites many examples of internal development including Amazon’s investments in robotics and speech recognition, and Salesforce on virtual agents and machine learning. BMW, Tesla, and Toyota lead auto manufacturers in their investments in robotics and machine learning for use in driverless cars. Toyota is planning to invest $1B in establishing a new research institute devoted to AI for robotics and driverless vehicles.
  • McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area with the Compound Annual Growth Rate (CAGR) reaching approximately 80% from 20-13 to 2016. The following graphic illustrates the distribution of external investments by category from the study.
  • High tech, telecom, and financial services are the leading early adopters of machine learning and AI. These industries are known for their willingness to invest in new technologies to gain competitive and internal process efficiencies. Many startups have also had their start by concentrating on the digital challenges of this industries as well. The MGI Digitization Index is a GDP-weighted average of Europe and the United States. See Appendix B of the study for a full list of metrics and explanation of methodology. McKinsey also created an overall AI index shown in the first column below that compares key performance indicators (KPIs) across assets, usage, and labor where AI could make a contribution. The following is a heat map showing the relative level of AI adoption by industry and key area of asset, usage, and labor category.
  • McKinsey predicts High Tech, Communications, and Financial Services will be the leading industries to adopt AI in the next three years. The competition for patents and intellectual property (IP) in these three industries is accelerating. Devices, products and services available now and on the roadmaps of leading tech companies will over time reveal the level of innovative activity going on in their R&D labs today. In financial services, for example, there are clear benefits from improved accuracy and speed in AI-optimized fraud-detection systems, forecast to be a $3B market in 2020. The following graphic provides an overview of sectors or industries leading in AI addition today and who intend to grow their investments the most in the next three years.
  • Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins as a result of AI adoption.McKinsey found that companies who benefit from senior management support for AI initiatives have invested in infrastructure to support its scale and have clear business goals achieve 3 to 15% percentage point higher profit margin. Of the over 3,000 business leaders who were interviewed as part of the survey, the majority expect margins to increase by up to 5% points in the next year.
  • Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing according to the McKinsey study. “Click to ship” cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment
  • Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide. Netflix found that customers, on average, give up 90 seconds after searching for a movie. By improving search results, Netflix projects that they have avoided canceled subscriptions that would reduce its revenue by $1B annually.

 

Original article here.


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The tech industry is dominated by 5 big companies — here’s how each makes its money

2017-05-26 - By 

More and more, everything crucial about the present and future of consumer tech runs through at least one five companies: Alphabet, Apple, Facebook, Amazon, and Microsoft.

Smartphones, laptops, app distribution, voice assistants and AI, streaming music and video, cloud computing, online shopping, advertising — whatever it is, chances are it runs through the oligopoly in some way. The list of startups that have bought by the big five, meanwhile, is almost too long to count.

Each of the five make great products, to be clear, but it’s hard to deny that they control how tech money flows.

How each of those companies make their revenues, though, varies wildly. As this recent chart from Visual Capitalist shows, each of the big five hold their empires on the back of different industries. Google’s parent company Alphabet, for all the dabbling it does, is an online advertising company first and foremost. Facebook is, too. Apple is a hardware company through and through, while everything about Amazon flows from its e-commerce business.

Though it’s still the dominant player in PCs, Microsoft stands out as the only tech giant with diversified sources of revenue. It has Windows, of course, but with the PC market in decline, it’s also getting significant gains from Office, the Azure cloud business, Xbox, Ads, and various other businesses.

Original article here.


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AWS dominates cloud computing, bigger than IBM/Google/Microsoft combined

2017-02-12 - By 

Amazon’s cloud provider is the biggest player in the rapidly growing cloud infrastructure market, according to new data.

Amazon Web Services (AWS) accounts for one third of the cloud infrastructure market, more than the value generated by its next three biggest rivals combined.

AWS dominates, with a 33.8 percent global market share, while its three nearest competitors — Microsoft, Google, and IBM — together accounted for 30.8 percent of the market, according to calculations by analyst Canalys.

The four leading service providers were followed by Alibaba and Oracle, which made up 2.4 percent and 1.7 percent of the total respectively, with rest of the market made up of a number of smaller players.

According to the researchers, total spending on cloud infrastructure services, which stood at $10.3bn in the fourth quarter of last year (up 49 percent year-on-year) will hit $55.8bn in 2017 — up 46 percent on 2016’s total of $38.1bn.

Continuing demand is leading the cloud companies to accelerate their data centre expansion. Canalys said AWS launched 11 new availability zones globally in 2016, four of which were established in Canada and the UK in the past quarter. IBM also opened its new data centre in the UK, bringing its total cloud data centres to 50 worldwide, while Microsoft also added with new facilities in the UK and Germany.

Google and Oracle set up their first infrastructure in Japan and China respectively, aiming at expanding their footprint in the Asia Pacific region, while Alibaba also unveiled the availability of its four new data centres in Australia, Japan, Germany, and the United Arab Emirates.

Strict data sovereignty laws — under which personal data has to be stored in servers that are physically located within the country — mean cloud service providers have to build data centres in key markets, such as Germany, Canada, Japan, the UK, China, and the Middle East, said Canalys research analyst Daniel Liu.

Original article here.


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All the Big Players Are Remaking Themselves Around AI

2017-01-02 - By 

FEI-FEI LI IS a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can “see.” And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos—technology that has become commonplace on the world’s biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world’s biggest tech companies are remaking themselves around this breed of artificial intelligence.

Alongside a former Stanford researcher—Jia Li, who more recently ran research for the social networking service Snapchat—the China-born Fei-Fei will lead a team inside Google’s cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

Google is not alone in this rapid re-orientation. Amazon is building a similar group cloud computing group for AI. Facebook and Twitter have created internal groups akin to Google Brain, the team responsible for infusing the search giant’s own tech with AI. And in recent weeks, Microsoft reorganized much of its operation around its existing machine learning work, creating a new AI and research group under executive vice president Harry Shum, who began his career as a computer vision researcher.

Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these changes are partly about marketing—efforts to ride the AI hype wave. Google, for example, is focusing public attention on Fei-Fei’s new group because that’s good for the company’s cloud computing business. But Etzioni says this is also part of very real shift inside these companies, with AI poised to play an increasingly large role in our future. “This isn’t just window dressing,” he says.

The New Cloud

Fei-Fei’s group is an effort to solidify Google’s position on a new front in the AI wars. The company is challenging rivals like Amazon, Microsoft, and IBM in building cloud computing services specifically designed for artificial intelligence work. This includes services not just for image recognition, but speech recognition, machine-driven translation, natural language understanding, and more.

Cloud computing doesn’t always get the same attention as consumer apps and phones, but it could come to dominate the balance sheet at these giant companies. Even Amazon and Google, known for their consumer-oriented services, believe that cloud computing could eventually become their primary source of revenue. And in the years to come, AI services will play right into the trend, providing tools that allow of a world of businesses to build machine learning services they couldn’t build on their own. Iddo Gino, CEO of RapidAPI, a company that helps businesses use such services, says they have already reached thousands of developers, with image recognition services leading the way.

When it announced Fei-Fei’s appointment last week, Google unveiled new versions of cloud services that offer image and speech recognition as well as machine-driven translation. And the company said it will soon offer a service that allows others to access to vast farms of GPU processors, the chips that are essential to running deep neural networks. This came just weeks after Amazon hired a notable Carnegie Mellon researcher to run its own cloud computing group for AI—and just a day after Microsoft formally unveiled new services for building “chatbots” and announced a deal to provide GPU services to OpenAI, the AI lab established by Tesla founder Elon Musk and Y Combinator president Sam Altman.

The New Microsoft

Even as they move to provide AI to others, these big internet players are looking to significantly accelerate the progress of artificial intelligence across their own organizations. In late September, Microsoft announced the formation of a new group under Shum called the Microsoft AI and Research Group. Shum will oversee more than 5,000 computer scientists and engineers focused on efforts to push AI into the company’s products, including the Bing search engine, the Cortana digital assistant, and Microsoft’s forays into robotics.

The company had already reorganized its research group to move quickly into new technologies into products. With AI, Shum says, the company aims to move even quicker. In recent months, Microsoft pushed its chatbot work out of research and into live products—though not quite successfully. Still, it’s the path from research to product the company hopes to accelerate in the years to come.

“With AI, we don’t really know what the customer expectation is,” Shum says. By moving research closer to the team that actually builds the products, the company believes it can develop a better understanding of how AI can do things customers truly want.

The New Brains

In similar fashion, Google, Facebook, and Twitter have already formed central AI teams designed to spread artificial intelligence throughout their companies. The Google Brain team began as a project inside the Google X lab under another former Stanford computer science professor, Andrew Ng, now chief scientist at Baidu. The team provides well-known services such as image recognition for Google Photos and speech recognition for Android. But it also works with potentially any group at Google, such as the company’s security teams, which are looking for ways to identify security bugs and malware through machine learning.

Facebook, meanwhile, runs its own AI research lab as well as a Brain-like team known as the Applied Machine Learning Group. Its mission is to push AI across the entire family of Facebook products, and according chief technology officer Mike Schroepfer, it’s already working: one in five Facebook engineers now make use of machine learning. Schroepfer calls the tools built by Facebook’s Applied ML group “a big flywheel that has changed everything” inside the company. “When they build a new model or build a new technique, it immediately gets used by thousands of people working on products that serve billions of people,” he says. Twitter has built a similar team, called Cortex, after acquiring several AI startups.

The New Education

The trouble for all of these companies is that finding that talent needed to drive all this AI work can be difficult. Given the deep neural networking has only recently entered the mainstream, only so many Fei-Fei Lis exist to go around. Everyday coders won’t do. Deep neural networking is a very different way of building computer services. Rather than coding software to behave a certain way, engineers coax results from vast amounts of data—more like a coach than a player.

As a result, these big companies are also working to retrain their employees in this new way of doing things. As it revealed last spring, Google is now running internal classes in the art of deep learning, and Facebook offers machine learning instruction to all engineers inside the company alongside a formal program that allows employees to become full-time AI researchers.

Yes, artificial intelligence is all the buzz in the tech industry right now, which can make it feel like a passing fad. But inside Google and Microsoft and Amazon, it’s certainly not. And these companies are intent on pushing it across the rest of the tech world too.

Original article here.

 


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Microsoft Researchers Predict What’s Coming in AI for the Next Decade

2016-12-06 - By 

Microsoft Research’s female contingent makes their calls for AI breakthroughs to come.

Seventeen Microsoft researchers—all of whom happen to be women this year—have made their calls for what will be hot in the burgeoning realm of artificial intelligence (AI) in the next decade.

Ripping a page out of the IBM IBM -0.48% 5 for 5 playbook, Microsoft MSFT -0.37% likes to use these annual predictions to showcase the work of its hotshot research brain trust. Some of the picks are already familiar. One is about how advances in deep learning—which endows computers with human-like thought processes—will make computers or other smart devices more intuitive and easier to use. This is something we’ve all heard before, but the work is not done, I guess.

 

For example, “the search box” most of us use on Google or Bing search engines will disappear, enabling people to search for things based on spoken commands, images, or video, according to Susan Dumais, distinguished scientist and deputy managing director of Microsoft’s Redmond, Wash. research lab. That’s actually already happening with products like Google GOOG -0.10% Now, Apple Siri AAPL 0.26% , and Microsoft Cortana—but there’s more to do.

Dumais says the box will go away. She explains:

That is more ubiquitous, embedded and contextually sensitive. We are seeing the beginnings of this transformation with spoken queries, especially in mobile and smart home settings. This trend will accelerate with the ability to issue queries consisting of sound, images or video, and with the use of context to proactively retrieve information related to the current location, content, entities or activities without explicit queries.

Virtual reality will become more ubiquitous as researchers enhance better “body tracking” capabilities, says Mar Gonzalez Franco, a researcher at the Redmond research lab. That will enable such rich, multi-sensorial experiences that could actually cause subjects to hallucinate. That doesn’t sound so great to some, but that capability could help people with disabilities “retrain” their perceptual systems, she notes.

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There’s but one mention on this list of the need for ethical or moral guidelines for the use of AI. That comes from Microsoft distinguished scientist Jennifer Chayes.

Chayes, who is also managing director of Microsoft’s New England and New York City research labs, thinks AI can be used to police the ethical application of AI.

Our lives are being enhanced tremendously by artificial intelligence and machine learning algorithms. However, current algorithms often reproduce the discrimination and unfairness in our data and, moreover, are subject to manipulation by the input of misleading data. One of the great algorithmic advances of the next decade will be the development of algorithms which are fair, accountable and much more robust to manipulation.

Microsoft experienced the mis-use of AI’s power first-hand earlier this year when its experimental Tay chatbot offended many Internet users with racist and sexist slurs that the program was taught by others. Microsoft chose to focus on female researchers to stress that, while women and girls make up half of the world’s population, they account for less than 20% of computer science graduates.

This is particularly true for women and girls who comprise 50% of the world’s population, but account for less than 20 percent of computer science graduates, according to the Organization for Economic Cooperation and Development. The fact that the U.S. Bureau of Labor Statistics expects that there will be fewer than 400,000 qualified applicants to take on 1.4 million computing jobs in 2020 means there is great opportunity for women in technology going forward.

Original article here.


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Google, Facebook, and Microsoft Are Remaking Themselves Around AI

2016-11-24 - By 

FEI-FEI LI IS a big deal in the world of AI. As the director of the Artificial Intelligence and Vision labs at Stanford University, she oversaw the creation of ImageNet, a vast database of images designed to accelerate the development of AI that can “see.” And, well, it worked, helping to drive the creation of deep learning systems that can recognize objects, animals, people, and even entire scenes in photos—technology that has become commonplace on the world’s biggest photo-sharing sites. Now, Fei-Fei will help run a brand new AI group inside Google, a move that reflects just how aggressively the world’s biggest tech companies are remaking themselves around this breed of artificial intelligence.

Alongside a former Stanford researcher—Jia Li, who more recently ran research for the social networking service Snapchat—the China-born Fei-Fei will lead a team inside Google’s cloud computing operation, building online services that any coder or company can use to build their own AI. This new Cloud Machine Learning Group is the latest example of AI not only re-shaping the technology that Google uses, but also changing how the company organizes and operates its business.

Google is not alone in this rapid re-orientation. Amazon is building a similar group cloud computing group for AI. Facebook and Twitter have created internal groups akin to Google Brain, the team responsible for infusing the search giant’s own tech with AI. And in recent weeks, Microsoft reorganized much of its operation around its existing machine learning work, creating a new AI and research group under executive vice president Harry Shum, who began his career as a computer vision researcher.

Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these changes are partly about marketing—efforts to ride the AI hype wave. Google, for example, is focusing public attention on Fei-Fei’s new group because that’s good for the company’s cloud computing business. But Etzioni says this is also part of very real shift inside these companies, with AI poised to play an increasingly large role in our future. “This isn’t just window dressing,” he says.

The New Cloud

Fei-Fei’s group is an effort to solidify Google’s position on a new front in the AI wars. The company is challenging rivals like Amazon, Microsoft, and IBM in building cloud computing services specifically designed for artificial intelligence work. This includes services not just for image recognition, but speech recognition, machine-driven translation, natural language understanding, and more.

Cloud computing doesn’t always get the same attention as consumer apps and phones, but it could come to dominate the balance sheet at these giant companies. Even Amazon and Google, known for their consumer-oriented services, believe that cloud computing could eventually become their primary source of revenue. And in the years to come, AI services will play right into the trend, providing tools that allow of a world of businesses to build machine learning services they couldn’t build on their own. Iddo Gino, CEO of RapidAPI, a company that helps businesses use such services, says they have already reached thousands of developers, with image recognition services leading the way.

When it announced Fei-Fei’s appointment last week, Google unveiled new versions of cloud services that offer image and speech recognition as well as machine-driven translation. And the company said it will soon offer a service that allows others to access to vast farms of GPU processors, the chips that are essential to running deep neural networks. This came just weeks after Amazon hired a notable Carnegie Mellon researcher to run its own cloud computing group for AI—and just a day after Microsoft formally unveiled new services for building “chatbots” and announced a deal to provide GPU services to OpenAI, the AI lab established by Tesla founder Elon Musk and Y Combinator president Sam Altman.

The New Microsoft

Even as they move to provide AI to others, these big internet players are looking to significantly accelerate the progress of artificial intelligence across their own organizations. In late September, Microsoft announced the formation of a new group under Shum called the Microsoft AI and Research Group. Shum will oversee more than 5,000 computer scientists and engineers focused on efforts to push AI into the company’s products, including the Bing search engine, the Cortana digital assistant, and Microsoft’s forays into robotics.

The company had already reorganized its research group to move quickly into new technologies into products. With AI, Shum says, the company aims to move even quicker. In recent months, Microsoft pushed its chatbot work out of research and into live products—though not quite successfully. Still, it’s the path from research to product the company hopes to accelerate in the years to come.

“With AI, we don’t really know what the customer expectation is,” Shum says. By moving research closer to the team that actually builds the products, the company believes it can develop a better understanding of how AI can do things customers truly want.

The New Brains

In similar fashion, Google, Facebook, and Twitter have already formed central AI teams designed to spread artificial intelligence throughout their companies. The Google Brain team began as a project inside the Google X lab under another former Stanford computer science professor, Andrew Ng, now chief scientist at Baidu. The team provides well-known services such as image recognition for Google Photos and speech recognition for Android. But it also works with potentially any group at Google, such as the company’s security teams, which are looking for ways to identify security bugs and malware through machine learning.

Facebook, meanwhile, runs its own AI research lab as well as a Brain-like team known as the Applied Machine Learning Group. Its mission is to push AI across the entire family of Facebook products, and according chief technology officer Mike Schroepfer, it’s already working: one in five Facebook engineers now make use of machine learning. Schroepfer calls the tools built by Facebook’s Applied ML group “a big flywheel that has changed everything” inside the company. “When they build a new model or build a new technique, it immediately gets used by thousands of people working on products that serve billions of people,” he says. Twitter has built a similar team, called Cortex, after acquiring several AI startups.

The New Education

The trouble for all of these companies is that finding that talent needed to drive all this AI work can be difficult. Given the deep neural networking has only recently entered the mainstream, only so many Fei-Fei Lis exist to go around. Everyday coders won’t do. Deep neural networking is a very different way of building computer services. Rather than coding software to behave a certain way, engineers coax results from vast amounts of data—more like a coach than a player.

As a result, these big companies are also working to retrain their employees in this new way of doing things. As it revealed last spring, Google is now running internal classes in the art of deep learning, and Facebook offers machine learning instruction to all engineers inside the company alongside a formal program that allows employees to become full-time AI researchers.

Yes, artificial intelligence is all the buzz in the tech industry right now, which can make it feel like a passing fad. But inside Google and Microsoft and Amazon, it’s certainly not. And these companies are intent on pushing it across the rest of the tech world too.

Original article here.


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Azure Stack will be a disruptive, game changing technology

2016-08-29 - By 

Few companies will use pure public or private cloud computing and certainly no company should miss the opportunity to leverage a combination. Hybrids of private and public cloud, multiple public cloud services and non-cloud services will serve the needs of more companies than any single cloud model and so it’s important that companies stop and consider their long term cloud needs and strategy.

Providing insight into the future of cloud computing is something that Pulsant has a lot of experience in and our focus on hybrid IT and hybrid services allows us to see where the adoption of public and private cloud benefits our customers’ strategies and requirements.

Since so much of IT’s focus in the recent past (and in truth, even now) has been on private cloud, any analytics that show the growth of public cloud give us a sense of how the hybrid idea will progress. The business use of SaaS is increasingly driving a hybrid model by default. Much of hybrid cloud use comes because of initial trials of public cloud services. As business users adopt more public cloud, SaaS in particular, they will need more support from companies, such as Pulsant, to help provide solutions for true integration and governance of their cloud.

Game changer

The challenge, as always in the cloud arena, is that there is no strict definition of the term ‘hybrid.’ There has been, until recently, a distinct lack of vendors and service providers able to offer simple solutions to some of the day-to-day challenges faced by most companies who are trying to develop a cloud strategy. Challenges include those of governance, security, consistent experiences between private and public services and the ability to simply ‘build once’ and ‘operate everywhere’.

Enter Azure Stack — it’s not often that I use language like “game changing” and “disruptive technology” but in the case of Azure Stack I don’t think these terms can be understated. For the first time you have a service provider (for that’s what Microsoft is becoming) that is addressing what hybrid IT really means and how to make it simple and easy to use.

So what is Azure Stack?

Few companies will use pure public or private cloud computing and certainly no company should miss the opportunity to leverage a combination. Hybrids of private and public cloud, multiple public cloud services and non-cloud services will serve the needs of more companies than any single cloud model and so it’s important that companies stop and consider their long term cloud needs and strategy.

Providing insight into the future of cloud computing is something that Pulsant has a lot of experience in and our focus on hybrid IT and hybrid services allows us to see where the adoption of public and private cloud benefits our customers’ strategies and requirements.

Since so much of IT’s focus in the recent past (and in truth, even now) has been on private cloud, any analytics that show the growth of public cloud give us a sense of how the hybrid idea will progress. The business use of SaaS is increasingly driving a hybrid model by default. Much of hybrid cloud use comes because of initial trials of public cloud services. As business users adopt more public cloud, SaaS in particular, they will need more support from companies, such as Pulsant, to help provide solutions for true integration and governance of their cloud.

Game changer

The challenge, as always in the cloud arena, is that there is no strict definition of the term ‘hybrid.’ There has been, until recently, a distinct lack of vendors and service providers able to offer simple solutions to some of the day-to-day challenges faced by most companies who are trying to develop a cloud strategy. Challenges include those of governance, security, consistent experiences between private and public services and the ability to simply ‘build once’ and ‘operate everywhere’.

Enter Azure Stack — it’s not often that I use language like “game changing” and “disruptive technology” but in the case of Azure Stack I don’t think these terms can be understated. For the first time you have a service provider (for that’s what Microsoft is becoming) that is addressing what hybrid IT really means and how to make it simple and easy to use.

So what is Azure Stack?

This is the simple question that completely differentiates Azure (public) / Azure Stack from a traditional VM-based environment. When you understand this, you understand how Azure Stack is a disruptive and game changing technology.

For a long time now application scalability has been achieved by simply adding more servers (memory, processors, storage, etc.) If there was a need for more capacity the answer was “add more servers”. Ten years ago, that still meant buying another physical server and putting it in a rack. With virtualisation (VMware, Hyper-V, OpenStack) it has been greatly simplified, with the ability to simply “spin-up” another virtual machine on request. Even this is now being superseded by the advent of cloud technologies.

Virtualisation may have freed companies from the need for having to buy and own hardware (capital drain and the constant need for upgrades) but with virtualisation companies still have the problem of the overhead of an operating system (Windows/Linux), possibly a core application (e.g. Microsoft SQL) and, most annoyingly, a raft of servers and software to patch, maintain and manage. Even with virtualisation there is a lot of overhead required to run applications as is the case when running dozens of “virtual machines” to host the applications and services being used.

The public cloud takes the next step and allows the aggregation of things like CPUs, storage, networking, database tiers, web tiers and simply allows a company to be allocated the amount of capacity it needs and applications are given the necessary resources dynamically. More importantly, resources can be added and removed at a moment’s notice without the need to add VMs or remove them. This in turn means less ‘virtual machines’ to patch and manage and so less overhead.

The point of Azure Stack is that it takes the benefits of public cloud and takes the next logical step in this journey — to bring the exact capabilities and services into your (private) data centre. This will enable a host of new ideas letting companies develop a whole Azure Stack ecosystem where:

  • Hosting companies can sell private Azure Services direct from their datacentres
  • System integrators can design, deploy and operate Azure solution once but deliver in both private and public clouds
  • ISVs can write Azure-compatible software once and deploy in both private and public clouds
  • Managed service providers can deploy, customise and operate Azure Stack themselves

I started by making the comment that I thought Azure Stack will be a disruptive, game changing technology for Pulsant and its customers. I believe that it will completely change how datacentres will manage large scale applications, and even address dev/test and highly secured and scalable apps. It will be how hosting companies like Pulsant will offer true hybrid cloud services in the future.

Original article here.


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Magic Quadrant for Cloud Infrastructure as a Service, Worldwide

2016-08-10 - By 

Summary

The market for cloud IaaS has consolidated significantly around two leading service providers. The future of other service providers is increasingly uncertain and customers must carefully manage provider-related risks.

Market Definition/Description

Cloud computing is a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using internet technologies. Cloud infrastructure as a service (IaaS) is a type of cloud computing service; it parallels the infrastructure and data center initiatives of IT. Cloud compute IaaS constitutes the largest segment of this market (the broader IaaS market also includes cloud storage and cloud printing). Only cloud compute IaaS is evaluated in this Magic Quadrant; it does not cover cloud storage providers, platform as a service (PaaS) providers, SaaS providers, cloud service brokerages (CSBs) or any other type of cloud service provider, nor does it cover the hardware and software vendors that may be used to build cloud infrastructure. Furthermore, this Magic Quadrant is not an evaluation of the broad, generalized cloud computing strategies of the companies profiled.

In the context of this Magic Quadrant, cloud compute IaaS (hereafter referred to simply as “cloud IaaS” or “IaaS”) is defined as a standardized, highly automated offering, where compute resources, complemented by storage and networking capabilities, are owned by a service provider and offered to the customer on demand. The resources are scalable and elastic in near real time, and metered by use. Self-service interfaces are exposed directly to the customer, including a web-based UI and an API. The resources may be single-tenant or multitenant, and hosted by the service provider or on-premises in the customer’s data center. Thus, this Magic Quadrant covers both public and private cloud IaaS offerings.

Cloud IaaS includes not just the resources themselves, but also the automated management of those resources, management tools delivered as services, and cloud software infrastructure services. The last category includes middleware and databases as a service, up to and including PaaS capabilities. However, it does not include full stand-alone PaaS capabilities, such as application PaaS (aPaaS) and integration PaaS (iPaaS).

We draw a distinction between cloud infrastructure as a service , and cloud infrastructure as an enabling technology ; we call the latter “cloud-enabled system infrastructure” (CESI). In cloud IaaS, the capabilities of a CESI are directly exposed to the customer through self-service. However, other services, including noncloud services, may be delivered on top of a CESI; these cloud-enabled services may include forms of managed hosting, data center outsourcing and other IT outsourcing services. In this Magic Quadrant, we evaluate only cloud IaaS offerings; we do not evaluate cloud-enabled services.

Gartner’s clients are mainly enterprises, midmarket businesses and technology companies of all sizes, and the evaluation focuses on typical client requirements. This Magic Quadrant covers all the common use cases for cloud IaaS, including development and testing, production environments (including those supporting mission-critical workloads) for both internal and customer-facing applications, batch computing (including high-performance computing [HPC]) and disaster recovery. It encompasses both single-application workloads and virtual data centers (VDCs) hosting many diverse workloads. It includes suitability for a wide range of application design patterns, including both cloud-native application architectures and enterprise application architectures.

Customers typically exhibit a bimodal IT sourcing pattern for cloud IaaS (see “Bimodal IT: How to Be Digitally Agile Without Making a Mess” and “Best Practices for Planning a Cloud Infrastructure-as-a-Service Strategy — Bimodal IT, Not Hybrid Infrastructure” ). Most cloud IaaS is bought for Mode 2 agile IT, emphasizing developer productivity and business agility, but an increasing amount of cloud IaaS is being bought for Mode 1 traditional IT, with an emphasis on cost reduction, safety and security. Infrastructure and operations (I&O) leaders typically lead the sourcing for Mode 1 cloud needs. By contrast, sourcing for Mode 2 offerings is typically driven by enterprise architects, application development leaders and digital business leaders. This Magic Quadrant considers both sourcing patterns and their associated customer behaviors and requirements.

This Magic Quadrant strongly emphasizes self-service and automation in a standardized environment. It focuses on the needs of customers whose primary need is self-service cloud IaaS, although this may be supplemented by a small amount of colocation or dedicated servers. In self-service cloud IaaS, the customer retains most of the responsibility for IT operations (even if the customer subsequently chooses to outsource that responsibility via third-party managed services).

Organizations that need significant customization or managed services for a single application, or that are seeking cloud IaaS as a supplement to a traditional hosting solution (“hybrid hosting”), should consult the Magic Quadrants for managed hosting instead ( “Magic Quadrant for Cloud-Enabled Managed Hosting, North America,” “Magic Quadrant for Managed Hybrid Cloud Hosting, Europe” and “Magic Quadrant for Cloud-Enabled Managed Hosting, Asia/Pacific” ). Organizations that want a fully custom-built solution, or managed services with an underlying CESI, should consult the Magic Quadrants for data center outsourcing and infrastructure utility services ( “Magic Quadrant for Data Center Outsourcing and Infrastructure Utility Services, North America,” “Magic Quadrant for Data Center Outsourcing and Infrastructure Utility Services, Europe” and “Magic Quadrant for Data Center Outsourcing and Infrastructure Utility Services, Asia/Pacific” ).

This Magic Quadrant evaluates all industrialized cloud IaaS solutions, whether public cloud (multitenant or mixed-tenancy), community cloud (multitenant but limited to a particular customer community), or private cloud (fully single-tenant, hosted by the provider or on-premises). It is not merely a Magic Quadrant for public cloud IaaS. To be considered industrialized, a service must be standardized across the customer base. Although most of the providers in this Magic Quadrant do offer custom private cloud IaaS, we have not considered these nonindustrialized offerings in our evaluations. Organizations that are looking for custom-built, custom-managed private clouds should use our Magic Quadrants for data center outsourcing and infrastructure utility services instead (see above).

Understanding the Vendor Profiles, Strengths and Cautions

Cloud IaaS providers that target enterprise and midmarket customers generally offer a high-quality service, with excellent availability, good performance, high security and good customer support. Exceptions will be noted in this Magic Quadrant’s evaluations of individual providers. Note that when we say “all providers,” we specifically mean “all the evaluated providers included in this Magic Quadrant,” not all cloud IaaS providers in general. Keep the following in mind when reading the vendor profiles:

  • All the providers have a public cloud IaaS offering. Many also have an industrialized private cloud offering, where every customer is on standardized infrastructure and cloud management tools, although this may or may not resemble the provider’s public cloud service in either architecture or quality. A single architecture and feature set and cross-cloud management, for both public and private cloud IaaS, make it easier for customers to combine and migrate across service models as their needs dictate, and enable the provider to use its engineering investments more effectively. Most of the providers also offer custom private clouds.

  • Most of the providers have offerings that can serve the needs of midmarket businesses and enterprises, as well as other companies that use technology at scale. A few of the providers primarily target individual developers, small businesses and startups, and lack the features needed by larger organizations, although that does not mean that their customer base is exclusively small businesses.

  • Most of the providers are oriented toward the needs of Mode 1 traditional IT, especially IT operations organizations, with an emphasis on control, governance and security; many such providers have a “rented virtualization” orientation, and are capable of running both new and legacy applications, but are unlikely to provide transformational benefits. A much smaller number of providers are oriented toward the needs of Mode 2 agile IT; these providers typically emphasize capabilities for new applications and a DevOps orientation, but are also capable of running legacy applications and being managed in a traditional fashion.

  • All the providers offer basic cloud IaaS — compute, storage and networking resources as a service. A few of the providers offer additional value-added capabilities as well, notably cloud software infrastructure services — typically middleware and databases as a service — up to and including PaaS capabilities. These services, along with IT operations management (ITOM) capabilities as a service (especially DevOps-related services) are a vital differentiator in the market, especially for Mode 2 agile IT buyers.

  • We consider an offering to be public cloud IaaS if the storage and network elements are shared; the compute can be multitenant, single-tenant or both. Private cloud IaaS uses single-tenant compute and storage, but unless the solution is on the customer’s premises, the network is usually still shared.

  • In general, monthly compute availability SLAs of 99.95% and higher are the norm, and they are typically higher than availability SLAs for managed hosting. Service credits for outages in a given month are typically capped at 100% of the monthly bill. This availability percentage is typically non-negotiable, as it is based on an engineering estimate of the underlying infrastructure reliability. Maintenance windows are normally excluded from the SLA.

  • Some providers have a compute availability SLA that requires the customer to use compute capabilities in at least two fault domains (sometimes known as “availability zones” or “availability sets”); an SLA violation requires both fault domains to fail. Providers with an SLA of this type are explicitly noted as having a multi-fault-domain SLA.

  • Very few of the providers have an SLA for compute or storage performance. However, most of the providers do not oversubscribe compute or RAM resources; providers that do not guarantee resource allocations are noted explicitly.

  • Many providers have additional SLAs covering network availability and performance, customer service responsiveness and other service aspects.

  • Infrastructure resources are not normally automatically replicated into multiple data centers, unless otherwise noted; customers are responsible for their own business continuity. Some providers offer optional disaster recovery solutions.

  • All providers offer, at minimum, per-hour metering of virtual machines (VMs), and some can offer shorter metering increments, which can be more cost-effective for short-term batch jobs. Providers charge on a per-VM basis, unless otherwise noted. Some providers offer either a shared resource pool (SRP) pricing model or are flexible about how they price the service. In the SRP model, customers contract for a certain amount of capacity (in terms of CPU and RAM), but can allocate that capacity to VMs in an arbitrary way, including being able to oversubscribe that capacity voluntarily; additional capacity can usually be purchased on demand by the hour.

  • Some of the providers are able to offer bare-metal physical servers on a dynamic basis. Due to the longer provisioning times involved for physical equipment (two hours is common), the minimum billing increment for such servers is usually daily, rather than hourly. Providers with a bare-metal option are noted as such.

  • All the providers offer an option for colocation, unless otherwise noted. Many customers have needs that require a small amount of supplemental colocation in conjunction with their cloud — most frequently for a large-scale database, but sometimes for specialized network equipment, software that cannot be licensed on virtualized servers, or legacy equipment. Colocation is specifically mentioned only when a service provider actively sells colocation as a stand-alone service; a significant number of midmarket customers plan to move into colocation and then gradually migrate into that provider’s IaaS offering. If a provider does not offer colocation itself but can meet such needs via a partner exchange, this is explicitly noted.

  • All the providers claim to have high security standards. The extent of the security controls provided to customers varies significantly, though. All the providers evaluated can offer solutions that will meet common regulatory compliance needs, unless otherwise noted. All the providers have SSAE 16 audits for their data centers (see Note 1). Some may have security-specific third-party assessments such as ISO 27001 or SOC 2 for their cloud IaaS offerings (see Note 2), both of which provide a relatively high level of assurance that the providers are adhering to generally accepted practices for the security of their systems, but do not address the extent of controls offered to customers. Security is a shared responsibility; customers need to correctly configure controls and may need to supply additional controls beyond what their provider offers.

  • Some providers offer a software marketplace where software vendors specially license and package their software to run on that provider’s cloud IaaS offering. Marketplace software can be automatically installed with a click, and can be billed through the provider. Some marketplaces also contain other third-party solutions and services.

  • All providers offer enterprise-class support with 24/7 customer service, via phone, email and chat, along with an account manager. Most providers include this with their offering. Some offer a lower level of support by default, but allow customers to pay extra for enterprise-class support.

  • All the providers will sign contracts with customers can invoice, and can consolidate bills from multiple accounts. While some may also offer online sign-up and credit card billing, they recognize that enterprise buyers prefer contracts and invoices. Some will sign “zero dollar” contracts that do not commit a customer to a certain volume.

  • Many of the providers have white-label or reseller programs, and some may be willing to license their software. We mention software licensing only when it is a significant portion of the provider’s business; other service providers, not enterprises, are usually the licensees. We do not mention channel programs; potential partners should simply assume that all these companies are open to discussing a relationship.

  • Most of the providers offer optional managed services on IaaS. However, not all offer the same type of managed services on IaaS as they do in their broader managed hosting or data center outsourcing services. Some may have managed service provider (MSP) or system integrator (SI) partners that provide managed and professional services.

  • All the evaluated providers offer a portal, documentation, technical support, customer support and contracts in English. Some can provide one or more of these in languages other than English. Most providers can conduct business in local languages, even if all aspects of service are English-only. Customers who need multilingual support will find it very challenging to source an offering.

  • All the providers are part of very large corporations or otherwise have a well-established business. However, many of the providers are undergoing significant re-evaluation of their cloud IaaS businesses. Existing and prospective customers should be aware that such providers may make significant changes to the strategy and direction of their cloud IaaS business, including replacing their current offering with a new platform, or exiting this business entirely in favor of partnering with a more successful provider.

In previous years, this Magic Quadrant has provided significant technical detail on the offerings. These detailed evaluations are now published in “Critical Capabilities for Public Cloud Infrastructure as a Service, Worldwide” instead.

The service provider descriptions are accurate as of the time of publication. Our technical evaluation of service features took place between January 2016 and April 2016.

Format of the Vendor Descriptions

When describing each provider, we first summarize the nature of the company and then provide information about its industrialized cloud IaaS offerings in the following format:

Offerings: A list of the industrialized cloud IaaS offerings (both public and private) that are directly offered by the provider. Also included is commentary on the ways in which these offerings deviate from the standard capabilities detailed in the Understanding the Vendor Profiles, Strengths and Cautions section above. We also list related capabilities of interest, such as object storage, content delivery network (CDN) and managed services, but this is not a comprehensive listing of the provider’s offerings.

Locations: Cloud IaaS data center locations by country, languages that the company does business in, and languages that technical support can be conducted in.

Recommended mode: We note whether the vendor’s offerings are likely to appeal to Mode 1 safety-and-efficiency-oriented IT, Mode 2 agility-oriented IT, or both. We also note whether the offerings are likely to be useful for organizations seeking IT transformation. This recommendation reflects the way that a provider goes to market, provides service and support, and designs its offerings. All such statements are specific to the provider’s cloud IaaS offering, not the provider as a whole.

Recommended uses: These are the circumstances under which we recommend the provider. These are not the only circumstances in which it may be a useful provider, but these are the use cases it is best used for. For a more detailed explanation of the use cases, see the Recommended Uses section below.

In the list of offerings, we state the basis of each provider’s virtualization technology and, if relevant, its cloud management platform (CMP). We also state what APIs it supports — the Amazon Web Services (AWS), OpenStack and vCloud APIs are the three that have broad adoption, but many providers also have their own unique API. Note that supporting one of the three common APIs does not provide assurance that a provider’s service is compatible with a specific tool that purports to support that API; the completeness and accuracy of API implementations vary considerably. Furthermore, the use of the same underlying CMP or API compatibility does not indicate that two services are interoperable. Specifically, OpenStack-based clouds differ significantly from one another, limiting portability; the marketing hype of “no vendor lock-in” is, practically speaking, untrue.

For many customers, the underlying hypervisor will matter, particularly for those that intend to run commercial software on IaaS. Many independent software vendors (ISVs) support only VMware virtualization, and those vendors that support Xen may support only Citrix XenServer, not open-source Xen (which is often customized by IaaS providers and is likely to be different from the current open-source version). Similarly, some ISVs may support the Kernel-based Virtual Machine (KVM) hypervisor in the form of Red Hat Enterprise Virtualization, whereas many IaaS providers use open-source KVM.

For a detailed technical description of public cloud IaaS offerings, along with a use-case-focused technical evaluation, see“Critical Capabilities for Public Cloud Infrastructure as a Service, Worldwide.”

We also provide a detailed list of evaluation criteria in “Evaluation Criteria for Cloud Infrastructure as a Service.” We have used those criteria to perform in-depth assessments of several providers: see “In-Depth Assessment of Amazon Web Services,” “In-Depth Assessment of Google Cloud Platform,” “In-Depth Assessment of SoftLayer, an IBM Company” and “In-Depth Assessment of Microsoft Azure IaaS.”

Recommended Uses

For each vendor, we provide recommendations for use. The most typical recommended uses are:

  • Cloud-native applications. These are applications specifically architected to run in a cloud IaaS environment, using cloud-native principles and design patterns.

  • E-business hosting. These are e-marketing sites, e-commerce sites, SaaS applications, and similar modern websites and web-based applications. They are usually internet-facing. They are designed to scale out and are resilient to infrastructure failure, but they might not use cloud transaction processing principles.

  • General business applications. These are the kinds of general-purpose workloads typically found in the internal data centers of most traditional businesses; the application users are usually located within the business. Many such workloads are small, and they are often not designed to scale out. They are usually architected with the assumption that the underlying infrastructure is reliable, but they are not necessarily mission-critical. Examples include intranet sites, collaboration applications such as Microsoft SharePoint and many business process applications.

  • Enterprise applications. These are general-purpose workloads that are mission-critical, and they may be complex, performance-sensitive or contain highly sensitive data; they are typical of a modest percentage of the workloads found in the internal data centers of most traditional businesses. They are usually not designed to scale out, and the workloads may demand large VM sizes. They are architected with the assumption that the underlying infrastructure is reliable and capable of high performance.

  • Development environments. These workloads are related to the development and testing of applications. They are assumed not to require high availability or high performance. However, they are likely to require governance for teams of users.

  • Batch computing. These workloads include high-performance computing (HPC), big data analytics and other workloads that require large amounts of capacity on demand. They do not require high availability, but may require high performance.

  • Internet of Things (IoT) applications. IoT applications typically combine the traits of cloud-native applications with the traits of big data applications. They typically require high availability, flexible and scalable capacity, interaction with distributed and mobile client devices, and strong security; many such applications also have significant regulatory compliance requirements.

For all the vendors, the recommended uses are specific to self-managed cloud IaaS. However, many of the providers also have managed services, as well as other cloud and noncloud services that may be used in conjunction with cloud IaaS. These include hybrid hosting (customers sometimes blend solutions, such as an entirely self-managed front-end web tier on public cloud IaaS, with managed hosting for the application servers and database), as well as hybrid IaaS/PaaS solutions. Even though we do not evaluate managed services, PaaS and the like in this Magic Quadrant, they are part of a vendor’s overall value proposition and we mention them in the context of providing more comprehensive solution recommendations.

Magic Quadrant

 
Figure 1. Magic Quadrant for Cloud Infrastructure as a Service, Worldwide
 
 
Source: Gartner (August 2016)
 
See original article here.
 
 

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