Monthly Archives: January 2020

Citrix Systems(NASDAQ:CTXS) posted better-than-expected fourth-quarter results. Yet at first sight, there's nothing to be excited about. Revenue increased to $810 million, up 1% year over year. And the near-term outlook doesn't look much brighter, since the midpoint of 2020 revenue guidance of $3.115 billion corresponds to 3.5% year-over-year growth.Also, management expects non-GAAP (adjusted) operating margin to decrease from 30% in 2019 to a range of 28% to 29%.

For a tech company that is expanding its business into the growing cloud computing market, this performance looks weak. But in the long term, Citrix should profit from this transition.

Citrix's legacy business consists of selling hardware and software that allows users to remotely access a virtual computer desktop hosted in a data center. Compared to a classic Windows 10 workstation, this solution offers the advantage of keeping the same desktop environment from any device, anywhere. Also, computer administrators benefit from the more efficient central management of employees' working environments.

But with the emerging cloud-computing technology, enterprises have been moving a part of their on-premise applications and infrastructure to the cloud, which makes Citrix's legacy portfolio less relevant. In response, Citrix integrated its solutions with the ecosystems of public cloud providers such as Microsoft'sAzure and Alphabet's Google Cloud. As a result, Citrix Workspace, the company's new platform, has become an attractive solution for mobile users working in a hybrid multi-cloud environment.

Image source: Getty Images.

This transformation involves short-term headwinds, though. With its legacy offerings, Citrix received up-front payments for selling perpetual licenses and multi-year maintenance. But with the shift to cloud-based solutions, Citrix switched to a subscription model. Thus, the company receives smaller annual recurring fees instead of large upfront payments, which negatively impacts its revenue and free cash flow in the short term.

As an illustration,fourth-quarter revenue from the product and license division and the support and services division dropped to $616 million, down 21% and 5% year over year, respectively. In contrast, revenue from the subscription division increased to $194 million, up 49% year over year.

As a result, revenue from the subscription segment increased to 24% of the company's total revenue during the last quarter,from 16.2% a year ago. And management expects revenue from subscriptions to represent 65% to 75% of the company's total revenue by 2024.

In the medium term, the headwinds resulting from this transition should wane, and revenue growth should accelerate. In fact, total deferred and unbilled revenue, an early indicator of revenue, jumped 15% year over year.

Management forecasts revenue growth to progressively increase to a range of 8% to 10% by 2024 and exceed 10% over the long term. Also, it expects non-GAAP operating margin to reach at least 34%.

However, even if you assume a flawless execution over the next several years, the company's valuation doesn't look cheap.

Data sources: Citrix Systems and Yahoo! Finance

The difference between the company's GAAP and non-GAAP results is mostly due to share-based compensation (SBC) that management excludes from its calculation of non-GAAP earnings. SBC isn't a cash expense but it still represents a real cost to shareholders in terms of dilution.

Thus, investors should value this tech stock based on its GAAP results. And since Citrix's forward GAAP P/E ratio of 36.5 doesn't provide any margin of safety, even when taking into account the forecasted revenue growth acceleration over the next several years, prudent investors should stay on the sidelines.

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Beyond Short-Term Headwinds, Citrix Is Set to Profit From Its Shift to the Cloud - Motley Fool

Its true that edge computing is hard to define and is running high on the hype scale. But research and surveys continue to indicate that this trend of processing data where its collected for better latency, cost savings and real-time analysis is an innovation with legs. There will be 75 billion IoT devices by 2025, according to Statista.

According to Spiceworks 2019 State of IT report, 32% of large enterprises with more than 5,000 employees are using edge computing, and an additional 33% plan to adopt it by 2020.Tied to the growth of edge computing is the advent of 5G wireless: 51 operators globally will start 5G services by 2020, according to Deloitte Global research from 2019.

The major cloud companies are also investing in the edge. The AWS Local Zonesservice allows single-digit latency connecting to computing resources in a metro environment, while Microsoft offers the Azure Stack Edge appliance and Google Cloud IoT is a complete set of tools to connect, process, store, and analyse data both at the edge and in the cloud. Its safe to say that edge computing is becoming mainstream andCIOs and their IT operations leaders should plan appropriately for it in 2020 and beyond.

Weve read plenty about the business benefits from edge computing: oil rig operators need to see critical sensor data immediately to prevent a disaster; marketers want to push instant coupons to shoppers while in the store; video security monitoring can catch a thief in the act and medical device alerts that ensure patient safety are just a few solid use cases for edge-based processing. Edge computing may save IT money on cloud and network bandwidth costs as data volumes keep exploding and the need to store every data point becomes harder to justify.

There are also implications for IT management and operations. Local processing of high volume data could provide faster insights to manage local devices and maintain high-quality business services when seconds make a difference - such as in the event of a critical server performance issue threatening the ecommerce site.

Today, IT operations teams are inundated with data from thousands of on-premise and cloud infrastructure components and an increasingly distributed device footprint. The truth is, only an estimated 1% of monitoring data is useful, meaning that it provides indications of behavior anomaly or predictions about forthcoming change events.

With edge monitoring, we can potentially program edge-based systems to process and send only that small sliver of actionable data to the central IT operations management system (ITOM), rather than transmitting terabytes of irrelevant data daily to the cloud or an on-premise server where it consumes storage and compute power.

The job of filtering out the highly-contextual data on the edge, where business occurs, can support real-time decisions for successfully running IT operations at speed and scaleregardless of what combination of on-premise, public cloud or private cloud infrastructure is in place. At the same time, ITOps will need to be a leader when it comes to minimising the risk of edge technology from a performance, security and privacy perspective. However, as detailed below, we are in the early stages of determining how to make this work in practice.

These are the ITOps realities for edge computing:

Edge devices are often small and infrequently designed with security in mind. More than 70 percent of edge devices dont mandate authentication for third-party APIs, and more than 60 percent dont encrypt data natively. So the attack surface in IoT and edge environments is now larger, and less secure. This is particularly worrisome when considering edge devices that collect personally identifiable information such as email, phone numbers, health data or financial formation like credit card data. IT operations will need to work closely with security and legal teams to map out the company-specific risk, governance and compliance requirements around managing edge data.

Companies need platforms that can instantly monitor and analyse edge-generated data. In the connectivity of tomorrow, billions of connected devices will be communicating machine-to-machine, and the addition or subtraction of connected devices will be possible at an unprecedented scale. In this environment, the ability to manage large volumes of connected devices and the information being exchanged between them will be critical. 5G acts as the unifying technology, bringing flow of information and the density of scale. We will see an influx of innovation in edge monitoring in the coming years.

As organisations move more data and application assets to edge computing environments, IT will need to devise new policies and thresholds for central processing and alerting of all this data. Applying AI-based automation is essential here, as manual efforts will have zero chance of keeping up with the volume of data filtering, analysis and response. We are entering the age of nano satellites, vis--vis SpaceX and OneWeb. These edge devices will transform the future of agriculture, energy, mining, transportation and finance due to their capabilities for sending insightful data in real-time to customers, wherever they are at any moment. IT operations will have its work cut out to understand and properly manage this evolving edge infrastructure.

If you havent already realised that DevOps is taking over software development and IT management, just wait for when edge goes mainstream. There will be no other way to manage change and deployments of edge technology without the agile, continuous integration and continuous delivery methodology of DevOps. It will be imperative for ITOps to adopt DevOps practices and tools to manage, monitor and deploy edge resources.

ITOps is at a crossroads, determining how much of the past is still relative and how much they will need to change to adapt to a distributed, hybrid cloud world that will soon include edge as a fundamental pillar of their digital strategy. Security, machine intelligence and DevOps will be crucial areas of expertise for ITOps teams looking to help drive better business value and customer experiences from the edge.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

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Edge computing and ITOps: Analysing the opportunities and challenges ahead - Cloud Tech

The hyperscale cloud providers Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform, with other pretenders occasionally cited naturally generate the vast majority of revenues and, with it, the headlines.

According to figures from Synergy Research in December, one third of data centre spend in Q3 ended up in hyperscalers pockets. The companys most recent market share analysis, again for Q3, found that for public infrastructure (IaaS) and platform as a service (PaaS), AWS held almost two fifths (39%) of the market, well ahead of Microsoft (19%) and Google (9%).

For those who say the race has long since been won, however, the course has gradually been changing as organisations explored hybrid and multi-cloud workflows, as well as tying infrastructure and platform together with software portfolios.

In Europe, the battleground is shifting rapidly. Each provider has planted their flag variously, aside from the hubs of London, Frankfurt et al. Google Cloud launched in Poland and Switzerland in 2019 making seven European locations in total, while Microsoft unveiled plans to launch Azure in Germany and Switzerland, also taking its European locations to seven. AWS, meanwhile, has six with two of these regions, Italy and Spain, due in early 2020 and 2023 respectively.

Companies are going deeper with Google and Microsoft when they embed the entirety of their SaaS capability around decision making for infrastructure as well

Nick McQuire, VP enterprise at CCS Insight, says that the competitive environment has obviously turned up a notch over the past 12 months. Even if you rewind 12 months, youre starting to see the significant gap that AWS had, particularly in the core infrastructure as a service, compute, storage, just slightly become minimised, he tells CloudTech. Obviously AWS is still very much a front runner, depending on how you define it but this is always part of the challenge in the industry.

Talk to any number of people and you will get any number of definitions as to who is doing what and where. This obfuscation is somewhat encouraged by the hyperscalers themselves. AWS discloses its specific revenues $8.99 billion for Q319 while Microsoft and Google do not.

Microsoft directs its financial reporting into three buckets; productivity and business processes ($11bn in Q120), intelligent cloud ($10.8bn), and more personal computing ($11.1bn). Azure growth percentages are wheeled out, but a specific figure is not; the overall figure lies somewhere in the first two categories. According to Jay Vleeschhouwer of Griffin Securities, per CNBC, Azures most recent quarter was estimated at $4.3bn. Google, meanwhile, puts its cloud operation as one part of its other revenues tag, which was $6.42bn last quarter. Analysts have been asking the company whether it will cut free the specific revenues, only to get a committed non-committal in response.

Yet therein lies the rub. Where do these revenues come from and how does it compare across the rest of the stack? As Paul Miller, senior analyst at Forrester, told this publication in February, the real value for Google, among others, is to assemble and reassemble various parts of its offerings to customers, from software, to infrastructure, and platform. That should be the story, not whether their revenue in a specific category is growing 2x, 3x, or 10x.

For McQuires part, this is the differentiation between Google and Microsoft compared with AWS. The alternative approach is where you see companies, typically from the CEO down, that are all-in on transformation, and seeing the workplace environment and internal side of the house as part of that, he says. Thats typically where you will see companies go a little bit deeper with a Google or Microsoft; they will embed the entirety of their SaaS applications capabilities in and around decision making for their infrastructure as a service as well.

That approach very much favours Microsoft, and weve seen more and more companies in the context of Microsofts big announcements last year.

With this in mind, McQuire sees the rise of the preferred cloud, as the marketing spiel would put it. AT&T and Salesforce were two relatively recent Microsoft customers whose migrations were illustrated by this word. It doesnt mean all-in, but neither does it really mean multi-cloud. Companies will start to entrench themselves around one strategic provider, as opposed to having one multiple cloud, and [being] not necessarily embedded business-wise into a strategic provider, says McQuire.

This represents a fascinating move with regards to the industrys progression. Part of the reason why many industries did little more than tip their toes into the cloud in the early days was down to the worry of vendor lock-in. Multi-cloud and hybrid changed that up, so should organisations be fearful again now? McQuire notes Microsoft has been doing a lot to change its previous image, yet a caveat remains.

Theres always going to be that pre-perceived notion among companies out there that they have to careful with going all-in with Microsoft around this, he admits. You see companies navigate through those complexities [but] I feel that theres a growing set of customers, particularly globally, and if theyre going with Azure theyre going heavily and quite deep with Microsoft across the piece, as opposed to taking a workload by workload Azure model.

While Google Cloud is seeing areas of success, particularly among high level services around machine learning, theres a longer game at play

According to a recent study from Goldman Sachs, more organisations polled were using Azure for cloud infrastructure versus AWS. Its worth noting that the twice-annual survey polls only 100 IT executives, but they are at Global 2000 companies. Per CNBC again, 56 execs polled used Azure, compared with 48 for AWS.

This again shows the wider strength of the ecosystem, according to McQuire. For the companies that are making more investments in the infrastructure as a service for Microsoft, theyre doing it with a complete picture in mind around the strength of these higher level services, particularly as you shift into SaaS applications and, more important, a lot of security and management capabilities, he says. McQuire adds that Microsoft has had success with Azure in the UK, for instance from the number of firms who have moved to Office 365 over the past few years.

Google Cloud, meanwhile, has had a particularly interesting 12 months. In terms of making noise, under the leadership of Thomas Kurian, the company has been especially vociferous. Its acquisitions from Looker to Alooma, from Elastifile to CloudSimple stood out, and even this year a raft of news has come through, from retail customers to storage and enterprise updates.

Expect more acquisitions to come out of Google Cloud in the coming year in what is going to be a long game. Despite the various moves made in terms of recruitment and acquisitions in beefing up Googles marketing and sales presence, plenty more is to come. Whilst clearly I think the focus is on improving Google Cloud and targeting very key areas and theyre seeing areas of success, particularly among high level services around machine learning theres a longer game at play, says McQuire. The question is: how much time do they have in this arena?

Theyre going to have to focus more and more on some of those higher-level services, as opposed to the commodity infrastructure as a service market, McQuire adds. I think its going to be an ongoing battle for Google for awareness in the industry, in the market, and more importantly, I think there is still a large number of customers who are just not that well educated on what Google is doing in this space.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

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The war rages on for AWS, Azure and Google Cloud: Exploring the battlefield and strategy for 2020 - Cloud Tech

The Intelligent Edge is gaining momentum in the embedded development community and beyond. To get a better understanding of what that means exactly, Embedded Computing Design spent some time with Wind Rivers Gareth Noyes, the companys Chief Strategy Officer.

Noyes: The simple answer is that an Intelligent Edgeis a place that spans from the device edge to the infrastructure edge where data is both collected and analyzed. This means that data is processed, analyzed and acted on before it is sent to the cloud. The result of the Intelligent Edgeis that costs are lower, network impact and latencies are minimized, and security risks are reduced. Ultimately, business is conducted in a more efficient manner.

Noyes: Companies that rely on highly centralized cloud and hybrid-cloud infrastructures realize the importance of the edge and the value it offers. Growing quantities of raw data now originate from IoT devices and sensors, and new classes of devicesfrom autonomous vehicles to industrial robotsrequire real-time access to operational data. In other words, data is being both generated and consumed at the edge, far from the centralized computing power of cloud-scale infrastructure. For all that data to make round trips to the cloud would devour too much bandwidth and take too much time for the operational needs of the edge devices. Thats why edge computing is becoming so important.

Noyes: The border between things (the edge) and the cloud is not easy to cross. Software development in the two worlds requires different skillsets, and largely uses different tools, languages, and methods. Expectations diverge about how long a development project will take and what its lifecycle should be.

And, cloud solutions dont transport easily to the edge. Companies run into unfamiliar and incompatible environments when they try to move processing closer to the data at the edge. The reasons for this are that edge computing resources are more limited, physical access and security present new challenges, and virtualization is not the norm. Resiliency, quality of service, and high availability are cornerstone data center requirements, but they can be far more challenging at the edge. Moreover, devices at the edge today are not standardized and interchangeable like servers in a data center. All this can make the edge a hostile environment for the cloud-native way of doing things.

Noyes: Edge computing devices that interact with real-time embedded systems can have specific requirements that are unfamiliar to cloud computing. Determinism is a good example. The deterministic model at the root of embedded systems is the expectation that a device will always complete the same task in the same way and in the same amount of time. Anything less than 100% determinism can result in catastrophic failure.

This is foreign territory to the data center, which is all about parallel processes that typically complete their tasks within the target timeframe. In the data center, you expect and accept a certain amount of jitter, a long tail of latency with outliers that miss the target. Deterministic embedded devices at the edge dont have this tolerance.

Noyes: Embedded systems have historically been about fixed-function, monolithic, cost-sensitive, compute-limited physical devices with long development cycles and long lifecycles. Embedded device builders struggle with how to accelerate development, scale production, and reimagine edge devices more broadly to be relevant and valuable to the world of cloud-scale infrastructure.

The pressure for device original equipment manufacturers (OEMs) to change comes both from above and below. Below are the hardware platforms that devices are built on. The availability and affordability of powerful new processing and storage options challenges device makers to take advantage of these capabilities. Today, it doesnt make sense to spend tremendous effort optimizing a custom, single-purpose piece of software to run on a specific processor. And with the advent of todays fast, multi-core, power-efficient CPUs, thats not necessary.

The pressure from above comes from customers who want flexible, multipurpose, interchangeable edge devices that are compatible with their cloud-like infrastructures. Cloud computing is expanding from the data center out to the edge, and it needs a place to land there. Pressure from above is also coming from the cloud developers who want to run their applications on edge devices. They dont want to have to learn new development languages or worry about the constrains of the system.

Noyes: Sure. We look at the landing zone concept as enabling the development of applications that can be deployed anywhere in the intelligent edge, irrespective of whether its a physical (embedded) edge device or part of the virtualized cloud-scale infrastructure.

Wind River has deep and broad experience delivering solutions for the intelligent edge. We understand embedded systems that make up the intelligent edge, we have experience deploying robust cloud-scale edge infrastructure, and we know how they can become more compatible with cloud-native applications. To create a landing zone architecture, OEMs will need to build new kinds of edge devices, consisting of a few critical, yet standard, building blocks enabling them to leverage the same modern application development processes to deploy new services at the edge.

For example, new devices and systems must decouple monolithic embedded systems with layers of abstraction, and there must be containers for traditional real-time operating-system (RTOS) applications and for a new class of cloud-native edge applications. Its also a good idea to utilize Agile development practices. As embedded development moves to a foundation of DevSecOps and continuous integration/continuous delivery (CICD), the skills gapand cultural gapwill narrow between OEMs and the customers they serve.

Noyes: Wind River is the only company with a robust and comprehensive embedded software portfolio to deliver this vision. We can help bring embedded OEMs and cloud-native industries together to architect a new intelligent edge infrastructure. This would be accomplished through a variety of software and tools already in the Wind River portfolio, including the VxWorks RTOS, Wind River Linux, the Helix Virtualization Platform that allows VxWorks and Linux to run concurrently with or without containers, Wind River Cloud Platform, Wind Rivers family of products for cloud-scale infrastructure, and Simics for system simulation.

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Executive Viewpoint: Migrating to the Intelligent Edge - Embedded Computing Design

For F1 fans, one of the most exciting aspects of the sport is seeing one car neck-and-neck with another, vying for pole position. While fans are already enthralled by the way this works today, those working at F1 wanted to be able to make this aspect of racing more exciting, by reducing the decrease in downforce generated by aerodynamics.

Currently, F1 cars suffer a loss of downforce when they are driving close to one another, meaning its harder for drivers to stay close to their rivals, and crucially, harder to overtake. For instance, a car running one car length behind another loses up to 40 per cent of its downforce. It means that the car slides around more and tires overheat.

In 2021 theres a key strategic opportunity coming for us, where we are making a raft of technical and sporting changes in F1 so when we looked at that, and asked our fanbase what they wanted, they said closer racing, wheel-to-wheel racing, and possibly more overtaking, F1s chief technical engineer Rob Smedley tells NS Tech.

When one car is front of another it create a large wake structure of turbulent air behind it which means cars are heavily affected by the cars in front of them.

What we came up with was to redesign the shape of the F1 car in order to reduce the wake effect onto the following car and therefore we redesigned the cars by using computational fluid dynamics (CFD) which is essentially a virtual car in a virtual space, Smedley says.

The redesign structure means that the wake structure comes off the car in front and then it disappears off into the air rather than disappearing straight onto the car behind, causing a massive loss in downforce.

The result is that with the 2021 car, the simulation indicates that it doesnt lose 40 per cent of downforce but it loses about 7 per cent.

It was taking F1 about four days to do each simulation using high-performance computing. But in order to build all of these models and execute them with an agile development process, the organisation had to look at alternatives even though it was already using state-of-the-art technology.

In order to be doing rapid developments, you want a parallel process with lots of different case iterations of designs and then get the results and then go back and tweak the design and get the result so youre constantly iterating designs to converge in what is the final design.

However, four days was too long so we worked with AWS to use their cloud computing services and cloud HPC [high-performance computing] services and we got more than ten times the number of cores that were available with what was state-of-the-art and thats got each simulation down to eight hours so the output of the tool is CFD, but theres no way we would be able to have the agile development and a converged solution by this point in the year without the AWS cloud and HPC services, he says.

The F1 had a deadline it had to make in order to freeze the regulations, to ensure theyre ready for 2021.

We would have arrived at the deadline with a much less iterated and refined solution [without AWS], and its not currently a perfect solution because weve made such seismic shifts but its a good solution, and having the speed and agility that AWS cloud computing services enabled made it possible to arrive at a much better solution, he says.

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How F1 is using AWS to enable more close-proximity driving and overtaking - NS Tech

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Healthcare Cloud Computing Market 2020- Significance, Market Overview, and Worldwide Market Outlook Till 2027 - NY Telecast 99

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Profiling Key Players: IBM (US), Carestream Health, Inc (US), athenahealth (US), CareCloud Corporation (US), Siemens Healthineers AG (Germany), eClinicalWorks (US), Koninklijke Philips N

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Cloud-Computing In Healthcare Market Booming Worldwide by 2020-2027 focuses on major key players IBM (US), Carestream Health, Inc (US), athenahealth...

Register for this live video webcast - Thursday, January 30, 10:00 AM PT. Ask the expert - get your questions about vendor relationships answered by industry leaders.

Edge computing offers enormous promise some say it may even supplant cloud computing. Certainly this emerging technology, in which sensors across the Web provide a torrent of data, is growing rapidly. A research report in August 2019 forecast a blistering 32% CAGR yearly increase between now and 2023, meaning the edge market will double in size.

Edge computing fuels many of the tech trends that are getting buzz today, including smart factories, smart grids, connected vehicles and more. While IoT has driven edge computing, the technology fueled by 5G will play an ever greater role in many sectors beyond IoT.

To provide insight into the future of this key technology, Ill speak with a leading expert, Bryan Beal, Senior Director, Strategy and Solution Innovation at VMware Telco and Edge Cloud group

Bryan Beal, Senior Director, Strategy and Solution Innovation at VMware

James Maguire, Managing Editor, Datamation moderator

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Get your data questions answered by leading experts.

How to Get Started with Artificial Intelligence

FEATURE|ByJames Maguire, January 13, 2020

Quantum Computing: The Biggest Announcement from CES

ARTIFICIAL INTELLIGENCE|ByRob Enderle, January 10, 2020

The Artificial Intelligence Index: AI Hiring, Data, Trends

FEATURE|ByJames Maguire, January 07, 2020

Artificial Intelligence in 2020: Urgency and Pragmatism

ARTIFICIAL INTELLIGENCE|ByJames Maguire, December 20, 2019

Intel Buys Habana And Gets Serious About Deep Learning AI

FEATURE|ByRob Enderle, December 17, 2019

Qualcomm And Rethinking the PC And Smartphone

ARTIFICIAL INTELLIGENCE|ByRob Enderle, December 06, 2019

Machine Learning in 2020

FEATURE|ByJames Maguire, December 06, 2019

Three Tactics Hi-Tech Companies Can Leverage to Drive Growth

FEATURE|ByGuest Author, November 11, 2019

Could IBM Watson Fix Facebook's 'Truth Problem'?

ARTIFICIAL INTELLIGENCE|ByRob Enderle, November 04, 2019

How Artificial Intelligence is Changing Healthcare

ARTIFICIAL INTELLIGENCE|ByJames Maguire, October 09, 2019

Artificial Intelligence Trends: Expert Insight on AI and ML Trends

ARTIFICIAL INTELLIGENCE|ByJames Maguire, September 17, 2019

12 Examples of Artificial Intelligence: AI Powers Business

FEATURE|ByJames Maguire, September 13, 2019

Top 8 Artificial Intelligence Software

FEATURE|ByCynthia Harvey, August 30, 2019

Artificial Intelligence Jobs in 2019

ARTIFICIAL INTELLIGENCE|ByLisa Morgan, July 19, 2019

What is Artificial Intelligence?

ARTIFICIAL INTELLIGENCE|BySamuel Greengard, May 24, 2019

Top 45 Artificial Intelligence Companies

|ByAndy Patrizio, May 24, 2019

AI vs. Machine Learning vs. Deep Learning

ARTIFICIAL INTELLIGENCE|ByCynthia Harvey, May 16, 2019

Artificial Intelligence in Healthcare: How AI Shapes Medicine

ARTIFICIAL INTELLIGENCE|ByLisa Morgan, March 08, 2019

Top Machine Learning Solutions

FEATURE|BySamuel Greengard, February 14, 2019

Google Machine Learning Engine: Product Overview and Insight

ARTIFICIAL INTELLIGENCE|BySamuel Greengard, February 14, 2019

The rest is here:

The Future of Edge Computing: Beyond IoT - Datamation