Monthly Archives: February 2022

If you ask Nick Ivanov about his research, he might tell you that he is creating an anti-corruption machine in response to the inequity and unfairness he has seen in the world around him. Ivanov, a doctoral candidate in the Department of Computer Science and Engineering, believes that decentralized computer systems can address these issues.Blockchain is a decentralized system. It is secure, but the security comes at the cost of performance. The cloud, too, is a centralized system. It offers high performance and significant data storage, but one shortcoming is the potential for trust abuse.

As a fellow in the 2020 Cloud Computing Fellowship, Ivanov saw an opportunity to bring a long-term vision to life. He set out to create a system that combines the best features of blockchain and cloud computing, which he calls Blockumulus.Blockumulus, named by combining the words blockchain and cumulus, is a groundbreaking system that combines the security of blockchain with the high performance of the cloud.

Blockumulus allows us to preserve decentralization but make it much faster, explains Ivanov.

Existing research tries to address the limitations of blockchain by fixing the blockchain itself. The resulting improvements are still insufficient to meet large-scale demand. For example, Ivanov said, none of the existing blockchains are capable of replacing credit card transactions.

In our research, we use a completely different approach, he said. Instead of fixing blockchain, we try to run smart contracts on the cloud and use blockchain to secure this process.

Ivanov has demonstrated that Blockumulus can process tens of thousands of simultaneous transactions, which is similar to the throughput of credit card transactions in the world.

Ivanov credits the Cloud Computing Fellowship, run by the Institute for Cyber-Enabled Research and the IT Services Analytics and Data Solutions group, for providing the resources needed for him to execute the research he had imagined for so long.

I am very thankful for the opportunity to implement the project that would otherwise be impossible, Ivanov said.

Follow communications from ICER for more information about the Cloud Computing Fellowship and how you can get involved.

View original post here:

Combining the best of blockchain and cloud computing - MSUToday

When we talk about quantum computing, there is always the focus on what the quantum part of the solution is. Alongside those qubits is often a set of control circuitry, and classical computing power to help make sense of what the quantum bit does in this instance, classical computing is our typical day-to-day x86 or Arm or others with ones and zeros, rather than the wave functions of quantum computing. Of course, the drive for working quantum computers has been a tough slog, and to be honest, Im not 100% convinced its going to happen, but that doesnt mean that companies in the industry arent working together for a solution. In this instance, we recently spoke with a quantum computing company called Rigetti, who are working with Ampere Computing who make Arm-based cloud processors called Altra, who are planning to introduce a hybrid quantum/classical solution for the cloud in 2023.

The striking thing about quantum computing has always been the extravagant hardware required a golden steampunk chandelier of tubes and cables all required to bring the temperature of the hardware down hundredths of a degree above absolute zero. This minimizes thermal effects on the elements of a quantum computer, known as the qubit. Depending on the type of qubit involved, those cables can carry microwave signals, and how the chandelier is constructed often determines how many qubits are involved.

Qubits are the quantum computational power, and the more you have (in theory) the more exponentially more computing power there is on tap. However, because quantum computing doesnt deal in absolutes, sometimes those qubits are used for resiliency, which is needed at such extreme environments. Youll find that quantum computers list an effective number of qubits equivalent to the computational power, rather than the actual physical number present. Beyond that, there are different types of Qubits.

Transmon qubits rely on superconducting electron pairs being controlled inside a three-dimensional cavity. A spin qubit controls individual electron spins with magnetic fields. Most companies use Transmon qubits (Google, IBM, Rigetti), whereas Intel dropped its Transmon development in favour of spin qubits. Exactly how many qubits a system needs to do useful work is a hot topic in the literature, although Google claims it has performed computation impossible on classical computing with only 53 physical Transmon qubits again, another hot topic for debate.

The ultimate goal of quantum computing is to enable computing resources that can solve classical problems whose compute requirements are impossible within reasonable time frames. The typical example is Shors Algorithm, to find prime factors of number (essentially solving the underlying basis for cryptography that should take millions of years) in seconds. Another example is solving a typically quantum-like system, such as chemistry and biochemical interactions. Also optimization, going beyond typical traveling salesman into machine learning the idea is that quantum computing can assist training or inference to check all possible answers, simultaneously.

Quantum computing has always been seen as a future horizon of where high-performance should go. However, it is one of those elements that always seems 10-20 years away. In the early 2000s it was seen as 10-20 years away, and the same is true today. However there are now more startups and funded ventures willing to put in more research to get these systems up and running. One of those is Rigetti, and today is an announcement of a collaboration with Ampere Computing.

For the last few years, there has been a focus in putting high-performance computational resources within reach of everyone. The offering of cloud computing, web services, and 1000s of processors at your fingertips has never been more real, or been more easy. With enough money in your bucket, the cloud providers make it easy to spin up resources for storage, networking, services, or compute. Cloud computing like this is designed to scale as and when you need it. Rigetti wants to do the same with quantum computing.

Rigetti Computing, founded in 2013, is a series C funded quantum computing startup with a public $200m investment to date. Late last year, it announced the start of its new scalable quantum computing infrastructure with a chip containing 40 transmon-style qubits, multiple chips can be embedded onto a single package for a single quantum computing chandelier. The goal of these designs is to accelerate machine learning, both for quantum compute and classical compute, and as a result, theyre partnering with Ampere Computing which makes the Altra Max Arm-based CPUs.

The goal of the partnership is to provide a cloud-native solution combining both classical and quantum computing. Spinning up an instance would include some qubits and some cores, allowing customers to use standard machine learning APIs that would be naturally split across the two types of hardware. In this heterogeneous combination, the goal is to take advantage of the quantum system to do what it does best, and then leverage the traditional compute resources with the Altra Max CPUs for machine learning scale out.

Rigetti says that its solution will scale to hundreds of qubits, while Ampere resources can scale as naturally as most compute can. Rigetti chose Ampere as a partner in this instance because of what the company can provide Ampere always states that its processors are cloud-native, or built for the cloud, and that its 128-core chip can provide 1024 cores in a traditional 2U server with Arm Neoverse N1 performance.

At this point of the partnership, Rigetti and Ampere are at work developing a combination system up and running. Right now, the Ampere CPUs are to be part of the coupled performance resource, although Rigetti says that there could be a time where Amperes hardware might replace the FPGAs in the control units of the quantum system itself. The partnership aims to start working on a proof of concept, creating a local-to-Rigetti example of a cloud-native hybrid quantum/classical infrastructure, and creating a software stack optimized for machine learning. Rigetti says that it is already working with customers interested in the co-design to give itself targets for software optimizations.

The timeline for the rollout is still early, with a proof-of-concept planned over the next few months, then deployment with tier 1 cloud partners through 2023. The idea is to initially work with key customers to help optimize their workflows to combine with the hardware. Then its simply a case of scale out more qubits for quantum, more CPUs for classical. Ampere is set to launch Siryn this year, its own custom Arm core built on next generation process node technology, and we were told that the scope is to bring in future Ampere generations as they are developed.

Rigetti says that it has made strides in enabling transmon qubits viable at scale. Intel dropped its transmon qubit program because it didnt think it could scale, but also because they could create spin qubits fairly easily (however, control is a different part of that story). Rigetti plans to scale to the hundreds of qubits, allowing cloud customers to take a chunk of however many qubits they need at the time. One issue I brought up with them is synchronicity, and it sounds like they have a system that, in a traditional sense, can be asynchronous to scale. Rigetti believes there are elements to machine learning, both training and inference, that will scale with qubit count in this way.

Is Quantum Computing still a distant hope? The promise here is a hybrid product, with quantum and classical resources, for cloud customers in 2023. I fully expect that to be a viable use case. However, as is always the question with quantum computing what problem is it solving, and is it better than classical?

Read more from the original source:

Ampere Goes Quantum: Get Your Qubits in the Cloud - AnandTech

DUBLIN, Feb. 14, 2022 /PRNewswire/ -- The "Global Cloud Computing in Higher Education Market" report has been added to ResearchAndMarkets.com's offering.

The global Cloud Computing in Higher Education market held a market value of USD 2,182.4 Million in 2020 and is forecasted to reach USD 8,779.1 Million by the year 2027. The market is anticipated to register a CAGR of 22% over the projected period.

Cloud computing in higher education assists teachers, administrators, and students in their education related activities. It helps teachers for uploading learning materials, students to access their homeworks, and administrators to easily collaborate with each other and save money on data storage. Increasing adoption of SaaS based cloud platforms in higher education and growing adoption of e-learning is anticipated to boost the market growth. Furthermore, rising IT spending on cloud infrastructure in education coupled with increasing application of quantum computing in education sector is also expected to fuel the market growth.

Despite the driving factors, cybersecurity and data protection risks are estimated to restrain the market growth. Also, lack of compliance to the SLA and legal & jurisdiction issue is estimated to negatively hamper the market growth. Furthermore, rigid design of cloud-based systems is also expected to hinder the market growth during the forecast period.

Growth Influencers:

Increasing adoption of SaaS based cloud platforms in higher education

Adoption of SaaS based cloud platforms have increased in many industries. In the higher education sector, adoption of these platforms increased rapidly during the COVID-19 pandemic. This is because the transition to virtual learning has driven many institutions for reevaluate the longevity of their technology stack. Furthermore, various advantages associated with SaaS based platform are expected to boost the market growth. These benefits include fewer IT demands & constraints on capacity, greater flexibility to meet needs, enhanced collaboration, less down time, data recovery, enhanced security, and predictable monthly expenses. Therefore, increasing adoption of SaaS based cloud platforms in higher education is estimated to fuel the market growth.

Regional Overview:

Based on region, the global Cloud Computing in Higher Education market is divided into Europe, North America, Asia, Middle East, Africa, and South America.

The North America region is expected to hold the largest market share of around 29% owing to the rising adoption of technologically advanced products in the U.S. and Canada. The Asia Pacific region is anticipated to witness the fastest growth rate of around 26.6% owing to growing awareness regarding cloud computing technologies in the region.

Competitive Landscape:

Key players operating in the global Cloud Computing in Higher Education market include Adobe Systems, Inc., Alibaba Group, Cisco Systems, Inc., International Business Machines (IBM) Corporation, Netapp, Oracle Corporation, NEC Corporation, Microsoft Corporation, VMware, Inc., Amazon Web Services, Inc., Ellucian Company L.P., Dell EMC, Salesforce.com, SAP, and Blackboard, among others.

The approximate market share of the top 4 players is near about 61%. These market players are engaged in mergers & acquisitions, collaborations, and new product launches to strengthen their market presence. For instance, in August 2021, Oracle was appointed by the Ministry of Electronics and Information Technology, Government of India (MeitY) for providing empanelled cloud infrastructure solutions.

The global Cloud Computing in Higher Education market report provides insights on the below pointers:

The global Cloud Computing in Higher Education market report answers questions such as:

Key Topics Covered:

Chapter 1. Research Framework

Chapter 2. Research Methodology

Chapter 3. Executive Summary: Global Cloud Computing in Higher Education Market

Chapter 4. Global Cloud Computing in Higher Education Market Overview4.1. Industry Value Chain Analysis4.1.1. Software Developers4.1.2. Technology Integrators4.1.3. Service Providers/Owners4.1.4. End-User4.2. Technology Lifecycle4.2.1. Early Adopters & Pioneers use cases 4.2.2. Digital Transformation Trend and Impact of Market Growth4.3. Porter's Five Forces Analysis4.3.1. Bargaining Power of Suppliers4.3.2. Bargaining Power of Buyers4.3.3. Threat of Substitutes4.3.4. Threat of New Entrants4.3.5. Degree of Competition4.4. PEST Analysis4.5. Market Dynamics and Trends4.5.1. Growth Drivers4.5.2. Restraints4.5.3. Challenges4.5.4. Key Trends4.6. Competition Dashboard4.6.1. Market Concentration Rate4.6.2. Company Market Share Analysis (%), 20204.6.3. Competitor Mapping4.7. Pricing Analysis

Chapter 5. Cloud Computing in Higher Education Market Analysis, By Institute Type5.1. Key Insights5.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)5.2.1. Universities5.2.2. Technical Schools5.2.3. Ivy League Schools (Universities)5.2.4. Community Colleges & Others

Chapter 6. Cloud Computing in Higher Education Market Analysis, By Ownership6.1. Key Insights6.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)6.2.1. Public Institutes6.2.2. Private Institutes6.2.3. Corporate Learning

Chapter 7. Cloud Computing in Higher Education Market Analysis, By Application7.1. Key Insights7.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)7.2.1. Administration7.2.1.1. Payments7.2.1.2. Calendar (Scheduling & Planning)7.2.1.3. Identity and Access Management7.2.2. Content/Document Storage & Management7.2.3. Unified Communication (Email, video conferencing/seminars)7.2.4. Others

Chapter 8. Cloud Computing in Higher Education Market Analysis, By Deployment8.1. Key Insights8.2. Market Size and Forecast, 2017 - 2027 (US$ Mn)8.2.1. Private Cloud8.2.2. Public Cloud 8.2.3. Hybrid Cloud 8.2.4. Community Cloud

Chapter 9. Global Cloud Computing in Higher Education Market Analysis, By Geography

Chapter 10. North America Cloud Computing in Higher Education Market Analysis10.1. Key Insights10.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type10.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application10.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership10.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment10.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 11 Europe Cloud Computing in Higher Education Market Analysis11.1 Key Insights11.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type11.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application11.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership11.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment11.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 12 Asia Pacific Cloud Computing in Higher Education Market Analysis12.1 Key Insights12.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type12.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application12.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership12.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment12.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 13 South America Cloud Computing in Higher Education Market Analysis13.1 Key Insights13.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type13.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application13.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership13.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment13.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 14 Middle East Cloud Computing in Higher Education Market Analysis14.1 Key Insights14.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type14.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application14.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership14.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment14.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 15 Africa Cloud Computing in Higher Education Market Analysis15.1. Key Insights15.2. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Institute Type15.3. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Application15.4. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Ownership15.5. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Deployment15.6. Market Size and Forecast, 2017 - 2027 (US$ Mn), By Country

Chapter 16. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook) *16.1. Adobe Systems, Inc.16.2. Alibaba Group16.3. Cisco Systems, Inc.16.4. International Business Machines (IBM) Corporation16.5. Netapp16.6. Oracle Corporation16.7. NEC Corporation16.8. Microsoft Corporation16.9. VMware, Inc.16.10. Amazon Web Services, Inc.16.11. Ellucian Company L.P.16.12. Dell EMC16.13. Salesforce.com16.14. SAP16.15. Blackboard

For more information about this report visit https://www.researchandmarkets.com/r/lygcly

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1904 Fax (outside U.S.): +353-1-481-1716

SOURCE Research and Markets

Read more:

Global Cloud Computing in Higher Education Market to 2027 - by Institute Type, Application, Ownership, Deployment and Region - PRNewswire

Bengaluru

Tech Mahindra Foundation, in collaboration with Amazon Internet Services, will offer training to unemployed and underemployed youth in technologies around cloud computing.

The 12-week, in-person training programme would be offered for free through Mahindra SMART Academies of Digital Technologies centres in Hyderabad, Mohali, Visakhapatnam, Bengaluru, Delhi, Mumbai, and Pune, Tech Mahindra said in a release.

Through scenario-based exercises, hands-on labs, and coursework, students would be able to learn to build programming language (Linux and Python), networking, security and relational database skills. The objective was to prepare candidates for an entry-level cloud role in the areas such as operations, site reliability, and infrastructure support, the company further said.

The training programme would also cover fundamental AWS Cloud skills as well as practical career skills, such as interviewing and resume writing, to help prepare individuals for an entry-level cloud position,

Rakesh Soni, CEO, Tech Mahindra Foundation said, Cloud computing is a 21st-century technological innovation that is enabling digital transformation. This programme will help learners build valuable and in-demand cloud computing skills, get them AWS Certifications, setting them up for a start.

Continue reading here:

TechM, Amazon to offer free training in cloud computing to unemployed youth - The Hindu

By BleepingComputer Deals

Microsoft Azure is one of the more useful cloud computing tools available, and getting certified in administration and architecture can open new doors for your IT career. The 2022 Microsoft Azure Architect & Administrator Exam Certification Prep Bundle helps you get up to speed on Microsoft Azure and how it works.

The bundle's instructors include Microsoft certified trainer and cloud architect Scott Duffy, twenty-year elearning veteran Ben Stretcha, and IT professional Vijay Saini. All of them have worked with Azure and have years of experience training, helping to bridge the gap between theory and practice in their courses.

The courses are broken out by the level of certifications. Those completely new to Azure should start with the AZ-900 certification, which reviews the fundamentals of both cloud computing in general, and Azure in particular. There's also an exam prep course for AZ-900, so you can get more practice.

If you're familiar with the basics of Azure, the bundle includes a module of project-based hands-on training to hone your skills, and a detailed look at storage in Azure, looking at real-world scenarios and solutions. System administrators curious how Azure can help them should start with an in-depth look at how Azure interacts with PowerShell and its task automation tools.

The bundle wraps up with a look at the next few levels of certification in Azure. AZ-204 explores solution development, AZ-303 develops your Azure architecture skills, and there's a full exam prep course for the AZ-304 exam. That lays the groundwork for both a broader scope of cloud computing skills and delve deeper into Azure.

Cloud computing is becoming a core skill for IT departments. The 2022 Microsoft Azure Architect & Administrator Exam Certification Prep Bundle offers a detailed overview for $39.99, a 97% discount off the $1800 MSRP.

Prices subject to change.

Disclosure: This is a StackCommerce deal in partnership with BleepingComputer.com. In order to participate in this deal or giveaway you are required to register an account in our StackCommerce store. To learn more about how StackCommerce handles your registration information please see the StackCommerce Privacy Policy. Furthermore, BleepingComputer.com earns a commission for every sale made through StackCommerce.

Read the original here:

Build better cloud systems with this Azure certification bundle - BleepingComputer

Amazon Web Services is beginning to expand its Local Zones for edge computing. (Image: AWS)

Amazon Web Services is going global with its edge computing ambitions. In a major expansion of its Local Zones program, AWS will deploy edge infrastructure in 32 cities around the world, building upon the 16 existing zones in the United States.

The huge expansion is a sign that investment in edge computing infrastructure is beginning to accelerate. The AWS announcement comes just a day after Akamai, one of the largest players in edge computing, said it would acquire Linode for $900 million to boost the reach and features of its distributed global network.

AWS says the Local Zones will help customers deploy low-latency applications in new markets, as well as meeting data residency requirements in regulated sectors like health care, financial services, and government.

The edge of the cloud is expanding and is now becoming available virtually everywhere, said Prasad Kalyanaraman, Vice President of Infrastructure Services at AWS. Thousands of AWS customers using U.S.-based AWS Local Zones are able to optimize low-latency applications designed specifically for their industries and the use cases of their customers. With the success of our first 16 Local Zones, we are expanding to more locations for our customers around the world who have asked for these same capabilities to push the edge of cloud services to new places.

Todays announcement fills in the details on the edge expansion announced at AWS re:Invent last November by CTO Werner Vogels, naming the new markets where AWS will deploy zones, along with a lineup of high-profile customers who will be early adopters, including Netflix and a cluster of gaming companies.

Amazon operates a massive global network of data centers to power its cloud computing platform, with most of its capacity focused on clusters of large campuses in key network hubs like Northern Virginia. With Local Zones, AWS is creating a more distributed infrastructure to supportedge computingand low-latency applications.

AWS OutpostsandLocal Zones are the key building blocks for Amazons edge computing strategy.AWS Outposts are racks filled with turn-key AWS cloud infrastructure, which allow enterprises to deploy hybrid clouds in their on-premises data centers. Outposts will also drive Amazons push into edge computing through Local Zones, which are regional facilities filled with Outposts.

AWS currently has Local Zones available in 16 North American markets; Atlanta, Boston, Chicago, Dallas, Denver, Houston, Kansas City, Las Vegas, Los Angeles, Miami, Minneapolis, New York City, Philadelphia, Phoenix, Portland, and Seattle.

Starting this year, new AWS Local Zones will launch in Amsterdam, Athens, Auckland, Bangkok, Bengaluru, Berlin, Bogot, Brisbane, Brussels, Buenos Aires, Chennai, Copenhagen, Delhi, Hanoi, Helsinki, Johannesburg, Kolkata, Lima, Lisbon, Manila, Munich, Nairobi, Oslo, Perth, Prague, Quertaro, Rio de Janeiro, Santiago, Toronto, Vancouver, Vienna, and Warsaw. The total deployment will happen across 2022 and 2023, the company said.

One of the best-known AWS customers is Netflix, which delivers streaming entertainment to 214 million paid memberships in over 190 countries. Weve previously written about Netflix interest in using edge computing to bring new efficiencies to TV and film production, changing the way huge video files are managed and shared. Netflix is now working with AWS to virtualize key parts of its visual effects operations, using low-latency cloud access to deliver virtual desktops for rendering and animation workloads.

(Visual) artists need specialized hardware and access to petabytes of images to create stunning visual effects and animations, said Stephen Kowalski, Director of Digital Production Infrastructure Engineering at Netflix. Historically, artists had specialized machines built for them at their desks; now, we are working to move their workstations to AWS to take advantage of the cloud. In order to provide a good working experience for our artists, they need low latency access to their virtual workstations.

AWS Local Zones brings cloud resources closer to our artists and have been a game changer for these applications, said Kowalski.By taking advantage of AWS Local Zones, we have migrated a portion of our content creation process to AWS while ensuring an even better experience for artists. We are excited about the expansion of AWS Local Zones globally, which brings cloud resources closer to creators, allowing artists to get to work anywhere in the world and create without boundaries.

AWS also shared examples of customers using Local Zones in other industries:

Read the original post:

AWS Steps Up Edge Investment, Will Add 32 Local Zones Across the World - Data Center Frontier

Dr. David Rhew, Microsofts global chief medical officer and VP of healthcare, has been studying how technology can improve health outcomes for nearly three decades.

His focus has long been on access to care, quality of care, patient safety, improving experiences for patients and providers, and finding ways to improve the overall efficiency of care.

He was previously CMO at Samsung and Zynx Health before that. But his role at Redmond, Washington-based Microsoft (NSDQ:MSFT) one of the worlds cloud-computing leaders offers his best opportunity yet to shape the future of medtech.

Rhew spoke withMedical Design & Outsourcingas part of an ongoing series of conversations about cloud computings contributions to medtech and the potential ahead. The discussion that follows hasbeen edited for space and clarity.

MDO: Whats an example of an existing cloud-connected device that might inspire medtech designers and engineers?

RHEW: A company weve been working with, Sensoria, makes a boot for diabetics. One of the biggest challenges with diabetics is foot ulcers. This boots sensors can help track pressure and movement and a variety of things and allows us to pull all that data into the cloud and apply advanced analytics and AI to potentially predict pressure ulcers.

MDO: What about cloud-connected devices of the future? From your perspective, whats possible in the next five or 10 years?

RHEW: There are sensors all over the place that we can use, and we can think of putting sensors of any type on any device, something you wear or implant in your body. From there, the question is what do you do with the data? And thats where artificial intelligence and machine learning can come in. In a surgical arena or perhaps a procedure room, if youre a clinician or a surgeon, youre using a set of devices that dont really have any sensors. If we can gather information about these devices which are now not just mechanical, but becoming electronic we can manage those devices in ways that we have in the past. Its sort of like a car back when you didnt have any sensors about when your oil was low, and your car just starts smoking. Today, we have a lot of devices where you dont know when its going to conk out, probably the last thing you want to happen during the middle of a surgery. A lot of the devices, when youre working on a particular patient, theres activity specific to that patient that should be captured. It could be how long the procedure was, the number of pushes of certain buttons when you think about how that information can be used, and youre looking to determine the optimal ways to perform a procedure, thats valuable information. We know the duration of a surgery has a direct impact on surgical outcomes. We now can get more granular.

And thats whats exciting because were starting to gather patient-specific information but can start also thinking about different devices and different procedures being done within, say, the surgical unit. We can then start pulling all that together and analyzing across the board, comparing hospitals to hospitals and providers to providers. There is so much variation in care, and a lot of times, its unwarranted and there are often best practices that can be gleaned. If we can understand what the benchmarks are and strive toward those, we can lead to better quality improvements. And that can only be done when you have data thats accessible at the granular level, roll it up into different areas, benchmark it and allow for AI to be applied on top of it to provide real-time guidance and support and improve patient outcomes.

MDO: What can the cloud offer medtech companies from the earliest stages of device design and prototyping through manufacturing and having them in use?

RHEW: Start with supply chain, your ability to understand at any particular point where certain things are, your ability to predict demand. Were already seeing that outside of healthcare. That is a critical part of manufacturing, a critical part of logistics, and will be critical for getting devices built in a timely manner. Start thinking about the actual device itself and the storage and compute capabilities. Theres a lot of data coming in, potentially real-time, maybe continuous. That information will be hard for a lot of existing systems to manage, and you certainly dont want that to pop into the EHR. Its just going to overload the system. We need high-performance-compute capability for some of these activities, cloud computing to synthesize information real-time. Were going to need edge computing as well because a lot of that information doesnt even need to go to the cloud simultaneously but can be stored and managed and then ultimately pushed in different time periods. If you think about what we have beyond even the advanced analytics and prediction capabilities, we can look at things such as image analysis, video analysis, ways that we can meta tag different images so we can find other images and search those and even reports with those images using text analytics. All of this information can be analyzed using tools for natural language processing and visual cloud computing that gives us a whole new robust set of data that you apply on top of the quantitative data sets. Combine that with the fact that weve got interoperability that allows us to pull in electronic health record data and others. This creates a brand new opportunity for us to start answering questions that weve always had about how to improve care.

MDO: Are there any cloud-enabled tools that are uniquely relevant for medtech?

RHEW: The ability for us to navigate with voice to access particular pieces of information is critical, and were starting to see it being used in clinical environments. Microsoft and Nuance are looking to come together, and we see natural language processing in particular voice as a key part of how healthcare professionals will be interfacing with their devices, with patients and with the data. If we have an opportunity to leverage voice, to improve and streamline that workflow, we now can gain user acceptance from clinicians, because its very tedious for clinicians to find information by typing it into a computer or asking somebody else to. But with voice as that intermediary, we have now an opportunity to access a lot of this information that is in there, its just hard to find.

MDO: How can companies get started with the cloud?

RHEW: We work with companies that are small, medium and large. On the small side, we have our Microsoft startups program, working directly with companies trying to build out their applications. When youre a small company and even a medium-sized company, theres a lot you do just because you have to create an end-to-end solution, but its not necessarily something thats very worthwhile for you to start building out. For example, mechanisms to ensure the security of your data and mechanisms to build FHIR (Fast Healthcare Interoperability Resources) APIs to connect into different systems. Yes, it should be done, but its a bit of a commodity and something thats probably best outsourced to a company that spends their entire day doing it. Microsoft can provide those type of platforms, the security, and we can provide the tools that allow you to build off of that. Now, if youre a large company and youre thinking about moving to the cloud, theres a lot of capabilities weve been building that are fairly unique and perhaps represent a significant opportunity to leverage. Some of the AI capabilities, the high-performance compute, those are all things that could be very difficult for organizations that arent in the space to be building and continuing to add capabilities such as voice and video and text analytics. But thats the type of stuff that often is essential if you want to take advantage of these newer data sets.

Microsoft is partnering with Johnson & Johnson on the cloud; read more about what the future holds in MDOs conversation with two leaders in Johnson & Johnsons medical device business.

The rest is here:

Microsoft CMO: Using the cloud to improve medtech design, manufacturing and care - Medical Design & Outsourcing