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“The first code for Neo4j and the property graph database was written in IIT Bombay”, said the chief Marketing Officer at Neo4j, Chandra Rangan.

In an exclusive interview with Analytics India Magazine, Rangan said that the database was structured and sketched on a napkin on a flight to Bombay by an intern, alongside Emil Eifrem—who is the founder and CEO of Neo4j—where they worked together to create the first code for its graph database platform. 

Rangan joined Neo4j as the chief marketing officer (CMO) on May 10, 2022. Prior to this, he worked at Google, running Google Cloud Platform product marketing and, more recently, product-led growth, strategy, and operations for Google Maps Platform. Rangan has over two decades of technology infrastructure experience across marketing leadership, strategy, and operations at Hewlett Packard Enterprise, Gartner, Symantec, McKinsey, and IBM. 

Founded in 2007, Neo4j has more than 700 employees globally. In June 2022, the company raised about $325 million in a Series F funding round led by Eurazeo, alongside participation from GV (formerly Google Ventures) and other existing investors like One Peak, Creandum, Greenbridge Partners, DTCP, and Lightrock

This is one of the largest investments in a private database company. It raised Neo4j’s valuation to over $2 billion. In contrast, even bigger than MongoDB, which raised a total of $311 million, and post-IPO, it raised about $192 million in IPO, making it worth $1.2 billion. 

Bets big on India 

With its latest funding round, Neo4j is looking to invest in expanding its footprint globally, and India is one of its top choices, thanks to a larger developer ecosystem, alongside a burgeoning startup ecosystem and IT service providers using its platform to offer solutions to global customers. 

Neo4j’s community edition, which is open source, is widely adopted by developers in the country. “We have an overall community of almost a quarter million users who are familiar with our platform”, said Rangan, explaining that it has one of the largest developers in the country. With the fresh infusion of funds, the company looks to tap into the market, expand its services, sales and support, and invest in the right strategies going forward. 

As part of its expansion plans, Neo4j started hiring in sales leadership and country manager roles from last year onwards and would also continue that momentum this year. “This is a big bet for us in multiple ways”, added Rangan, pointing at its Indian root and all the innovations in the country. 

Besides India, Neo4j has a strong presence in Silicon Valley and Sweden and has a huge developer ecosystem in the US, China, Europe, South East Asia and others. 

Strategies for expansion 

Over the years, Neo4j has grown through developers and some of the early adopters of its platform. “Unfortunately, developers interested in graph databases will typically start with us”, said Rangan affirmatively. 

Further, explaining the conversion cycle, he said that once they know about graph databases, they later join the community edition. Then, once they get comfortable with the use cases and start putting this into production, they eventually get into a paid version for the advanced security, support, scalability, and commercial constructs. 

“In India, that’s the similar motion we are seeing”, said Rangan. He revealed that they already have a huge developer community. Banking on this community, they plan to invest in continuing the engagement with the community in a meaningful way. 

Of late, the company has also started hiring several community leaders to encourage proactive engagement within the community. In addition, it is also investing heavily in sales and marketing engines, including technical sales, which work closely with organisations in building the use cases, alongside the implementation of services and support. 

What makes Neo4j special? 

One thing that makes Neo4j stand apart from other players is its intuitiveness in helping deploy applications faster because of its flexible schema. This helps developers to add properties, nodes, and more. “It gives tremendous flexibility for developers so they can get to the outcome much more quickly”, said Rangan. 

But what about the learning curve? Rangan said, “Literally, for a new developer, if they start learning graphs for the first time, it is very intuitive.” He explained that the learning curve is not that steep and doesn’t take long. “But, for folks who have been working in the development space and building applications and are very familiar and comfortable with RDBMS, i.e., rows and tables. Strangely enough, the learning curve is a little higher and steeper”, added Rangan, discussing that they have to unlearn to model intuitively versus modelling tables. He said the best way to overcome that learning curve is to try it out. 

“So, when you think about the learning curve, it is a very easy learning curve, especially if you can put aside the former way of thinking about things like rows and tables and go back to first principles.”—Chandra Rangan. 

Discovering use cases with Neo4j 

The International Consortium of Investigative Journalists (ICIJ) released the full list of companies and individuals in the Panama Papers, implicating at least 140 politicians from more than 50 countries in tax evasion schemes. The journalist used Neo4j to draw the relationship with their data and found common touchpoints and names of people involved in having multiple offshore accounts and evading tax. 

“We believe a whole bunch of sectors can actually get value. We have seen new sectors kind of pop up on a pretty regular basis”, said Rangan while citing various use cases in financial service sectors (fraud detection), healthcare (vaccine distribution), pharmaceuticals (drug discovery), supply chain and logistics (mapping automation), tech companies (managing IT networks), retail (recommendation systems), and more. 

Chandra Rangan further explained that people are still discovering what they can use graph databases for and how useful it is in some sense. He said that it is unleashing a whole bunch of innovations. “So, we are hoping for a lot of that to happen here in India because of the developer community”, he added. 

What’s next? 

Rangan said Neo4j would be aggressively investing in the community and ecosystem here in India. Besides this, he said they are investing in building a marketing and sales team, which has grown significantly in the last year. In addition, Neo4j is also investing in building a partner ecosystem to support a wider range of customers. 

“Depending on how quickly we can grow or cannot grow—again, responsible growth—we want to grow as fast as possible. But, we also want to make sure as we hire people as we establish the relationship, we are investing enough time, effort, and money to make sure that these relationships are successful”, concluded Rangan.

Sun, 07 Aug 2022 22:30:00 -0500 en-US text/html https://analyticsindiamag.com/the-origin-of-neo4j/
Killexams : Machine Learning as a Service (MLaaS) Market to Observe Exponential Growth By 2022 to 2030 | Google, IBM, Microsoft Amazon

New Jersey, United States-Machine Learning as a Service (MLaaS) Market 2022 – 2030, Size, Share, and Trends Analysis Research Report Segmented with Type, Component, Application, Region, and Forecast

The size of the machine learning as a service market worldwide was valued at $13.95 billion in 2020 and is projected to reach $302.67 billion by 2030, developing at a CAGR of 36.3% from 2021 to 2030. Machine learning is a course of data analysis that involves statistical data analysis performed to determine wanted prescient results without the implementation of express programming. It is intended to incorporate functionalities of artificial insight (AI) and mental registering including a progression of algorithms and is utilized to understand the relationship between datasets to obtain an ideal result. Machine learning as a service (MLaaS) incorporates a scope of administrations that deal with machine learning instruments through distributed computing services.

Major Machine learning as a Service Market Growth drivers incorporates an increased market for distributed computing and development associated with artificial insight and mental processing. Important impacting factors of the machine learning as a service remember development for demand for cloud-based arrangements, remembering development for demand for distributed computing, ascend in the adoption of analytical arrangements, development of artificial knowledge and mental registering market, increased application areas, and dearth of trained professionals.

Receive the demo Report of Machine Learning as a Service (MLaaS) Market Research Insights 2022 to 2030 @ https://www.infinitybusinessinsights.com/request_sample.php?id=809118

Increasing need to understand the client behavior, developing adoption of machine learning as a service (MLaaS) arrangements by small and medium-scale organizations and flood in the concentration on advancements in data science innovation are the major factors attributable to the development of the machine learning as a service (MLaaS) market.

MLaaS is considered as a sub-class of circulated registering administrations. MLaaS is a variety of administrations that offers an extensive variety of machine learning devices and parts to undertake operations with greater proficiency and viability. Increased demand for the web of things innovation will arise as the major market development driving factor. Developing advancements in artificial knowledge innovation will additionally aggravate the development of the market.

Segmentation

The machine learning as a service market is portioned into By Application, By Organization Size, By Component, and By End-Use Industry. Contingent upon part, the ML as a Service market is separated into software and services. Based on the organization size, it is separated into large endeavors and small and medium undertakings. On the basis of end-client industry, it is separated into aerospace and guard, BFSI, public area, retail, healthcare, IT and telecom, energy and utilities, manufacturing, and others. On the basis of application, it is separated into marketing and advertising, fraud discovery and chance management, prescient analytics, augmented and virtual reality, natural language handling, PC vision, security and surveillance, and others.

Based on components, the service segment dominated machine learning as a service market share and is supposed to maintain its dominance in the impending years. This is attributed to factors, for example, an increase in application areas and development associated with end-use enterprises among creating economies is supposed to drive the market development for machine learning services. Industry players are engaged in the implementation of technologically advanced answers for increased adoption of machine learning services. Utilization of machine learning services in the healthcare business for recognition of cancer as well as to check ECG and MRI increases the market in the healthcare area.

Regional Analysis

North America is the fast-developing district in the global machine learning as a service market, regarding technological advancements and adoption. It has an exceptional infrastructure and the ability to afford machine learning as a service arrangement. Besides, ascend in interests in the guard area, along with technological advancements in the telecommunication business, is supposed to drive the market development during the forecast time frame. Unofficial laws regarding data security are supposed to keep on being areas of strength for a for the machine learning services market. Services, for example, security information and cloud application are supposed to drive the market. In addition, solid presence of industry leaders like Google, IBM, Microsoft, and Amazon Web Services and enhanced item contributions have additionally prompted ascend in demand for machine learning around here. Moreover, development associated with artificial knowledge and mental registering is supposed to create lucrative open doors for the market players to leverage varied industry applications, for example, prescient analytics, natural language handling, PC vision, fraud recognition and management.

Key players
The key players of the industry are Google, IBM, Microsoft and Amazon among many other companies in the industry.

The following are some of the reasons why you should take a Machine Learning as a Service (MLaaS) market report:

  • The Report looks at how the Machine Learning as a Service (MLaaS) industry is likely to develop in the future.
  • Using Porter’s five forces analysis, it investigates several perspectives on the Machine Learning as a Service (MLaaS) market.
  • This Machine Learning as a Service (MLaaS) market study examines the product type that is expected to dominate the market, as well as the regions that are expected to grow the most rapidly throughout the projected period.
  • It identifies exact advancements, Machine Learning as a Service (MLaaS) market shares, and important market participants’ tactics.
  • It examines the competitive landscape, including significant firms’ Machine Learning as a Service (MLaaS) market share and accepted growth strategies over the last five years.
  • The research includes complete company profiles for the leading Machine Learning as a Service (MLaaS) market players, including product offers, important financial information, current developments, SWOT analysis, and strategies.

Explore the Full Index of the Machine Learning as a Service (MLaaS) Market Research Report 2022

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Thu, 16 Jun 2022 00:01:00 -0500 Newsmantraa en-US text/html https://www.digitaljournal.com/pr/machine-learning-as-a-service-mlaas-market-to-observe-exponential-growth-by-2022-to-2030-google-ibm-microsoft-amazon
Killexams : UTC And IBM Help Students Develop Job Skills For The Future

A new collaboration between The University of Tennessee at Chattanooga and IBM will deliver students an array of marketable skills in data analytics, one of the fastest growing sectors in the national and regional economy. Smart technologies, cloud computing, mobile platforms, social media and other new generation technologies are fueling the revolution of big data.

According to IBM’s exact Tech Trends Report, only 1 in 10 organizations has the skills needed to effectively apply advanced technologies such as business analyticsmobile computingcloud computing and social business.

In the U.S. alone, IT jobs are expected to grow by 22 percent through 2020 according to the US Bureau of Labor Statistics.

As part of the UTC-IBM relationship, faculty have access to the resources in IBM’s Academic Initiative. This program provides access to technology, curriculum and learning materials at no cost for faculty members around the world. IBM has also provided advanced education for UTC faculty members, which has resulted in the development of curriculum, according to Dr. Joseph Kizza, Professor and Head of the Department of Computer Science and Engineering. Kizza is currently teaching the course Big Data Analytics using IBM software, including Cognos, BigInsights and InfoSphereStreams.

“The collaboration with IBM is already helping the program to prepare graduating students with the most up-to-date and in-demand skills in the industry. This is going to help our students get better and more paying jobs. In the process, this will eventually help our recruitment,” explained Kizza.  “Additionally, we hold two Computer Science and Engineering (CSE) Showcases a year where we invite high school students to spend a Saturday with us and do hands-on labs. This opportunity will help us demonstrate the latest technologies in Big Data analytics, for example, which may increase or ignite students interests in attending UTC.”

BlueCross BlueShield of Tennessee, the city’s largest private employer, also collaborated by providing guidance on the curriculum and sharing insights on the real-world skills needed for success. The company believes the program will not only help meet its own need for technical talent, but provide a boost for the entire community.

“I see UTC becoming a leading academic institution for business analysis and data management—it will begin to produce the business intelligence experts Chattanooga companies need,” according to Brian Green, Manager of Business Intelligence and Performance Management at BlueCross. “Through this collaboration, local businesses will be able to tap into UTC’s academic offerings to make their companies more successful in critical new areas like Big Data analytics.”

He says students who take advantage of these opportunities can become “immediately marketable” in this fast-growing field. “They will graduate with hands-on experience in the IBM tools commonly used in the workplace.”

 

Mr.Green, who graduated from UTC in 1980, has been in the business of information management and system development in the insurance industry for more than 30 years.

“UTC has been involved in the IBM Academic Skills Cloud pilot program and we are proud to support the university’s efforts to provide students with data-driven education,” said Dan Hauenstein, director, IBM Academic Initiative. “Through the IBM Academic Initiative, students and faculty have access to industry leading technology and courseware to help develop the advanced big data analytics skills needed for jobs of the future.”

IBM’s Academic Initiative provides no charge access to curriculum, software and learning materials to more than 30,000 faculty members around the world. 

Tue, 12 Jul 2022 12:00:00 -0500 en text/html https://www.chattanoogan.com/2013/10/12/261192/UTC-And-IBM-Help-Students-Develop-Job.aspx
Killexams : Predictive Maintenance Can Benefit All Manufacturers

Predictive maintenance based on machine learning has reached the point where it can benefit virtually every manufacturer, big or small, an expert will tell engineers at the upcoming Pacific Design & Manufacturing Show.

Kayed Almasarweh, IBM’s Watson and cognitive solutions lead, contends that machine learning and artificial intelligence can minimize unplanned downtime, eliminate maintenance guesswork, optimize supply chain management, and reduce warranty costs in products, if used correctly. “This is not only for big manufacturing operations; it’s for everybody,” Almasarweh told Design News. “Once you get it implemented with the right data, you can get a return on investment almost immediately.”

Kayed Almasarweh of IBM: “This is not only for big manufacturing operations; it’s for everybody.” (Image source: IBM)

Almasarweh will provide a high-level view of predictive maintenance based on machine learning in a session titled, "Applying IoT and Machine Learning for Predictive Maintenance," at the Anaheim Convention Center on February 6th. At the session, he will discuss challenges, successes, and lessons learned using real-life examples from industry.

Today, Almasarweh said, applications for predictive maintenance are widespread and growing by the day. The technology could be applied to CNC machines, assembly robots, conveyor belts, stamping machines, chain rails, locomotives, trucks, and just about any other imaginable factory asset, he noted. “As long as the asset is used by a business to generate revenue, make products, or move things, it’s a target for predictive maintenance,” he told us.

To a large degree, predictive maintenance is being fueled by the broader availability of data in virtually all kinds of businesses, Almasarweh said. Many businesses have sensors in place, as well as access to data storage in the cloud. Moreover, central processing units (CPUs) and graphics processing units (GPUs) have improved so dramatically in the past decade that many can now analyze hundreds of millions of transactions per second. Together, the sensors, storage, and computing capabilities are creating a foundation for machine learning and artificial intelligence that hasn’t been available previously.

“Data is the heart of being able to do machine learning and AI,” Almasarweh said.

To get started, some manufacturers may need to bring in past information, such as data about previous machine failures and parameters that were captured along the way to those failures. In those cases, the data may be stored on electronic media, or even on paper, in files or folders.

Once the data is available, Almasarweh said, implementation of predictive maintenance involves one of two approaches. Manufacturers can purchase a machine learning model or develop one themselves. Those who develop their own models tend to be larger enterprises with bigger engineering staffs.

Either way, he said, the potential benefits are there. “If your maintenance costs are spinning out of control and it’s keeping your plant manager up at night, then you probably need to look at predictive maintenance,” he said. “It’s something that works and something that keeps getting better with time.”

Senior technical editor Chuck Murray has been writing about technology for 34 years. He joined Design News in 1987, and has covered electronics, automation, fluid power, and auto.

Tue, 14 Jun 2022 12:00:00 -0500 en text/html https://www.designnews.com/industrial-machinery/predictive-maintenance-can-benefit-all-manufacturers
Killexams : AI Tech Stocks and the Growing Implementation in the Sports Market

Vancouver, Kelowna and Delta, British Columbia--(Newsfile Corp. - July 21, 2022) - Investorideas.com (www.investorideas.com), a global investor news source covering Artificial Intelligence (AI) stocks releases a sector snapshot looking at the growing AI tech implementation in the sports market, featuring AI innovator GBT Technologies Inc. (OTC Pink: GTCH).

Read the full article at Investorideas.com

As with so many other sectors, the sports industry is seeing increasing penetration of Artificial Intelligence (AI) related technologies as aspects of the medium become more and more digitized. A recently published report from Vantage Market Research finds that the global market for AI in Sports is projected to grow from $1.62 billion USD in 2021 to $7.75 billion by 2028, registering a compound annual growth rate (CAGR) of 29.7 percent in the forecast period 2022-28. According to a market synopsis from the report, AI is being leveraged by a number of firms to track player performance, Improve the player's health, and to Improve sports planning.

One such firm is GBT Technologies Inc. (OTC Pink: GTCH), an early stage technology developer in IoT and Artificial Intelligence (AI) Enabled Mobile Technology Platforms, which recently completed phase one of its intelligent soccer analytics platform through its 50 percent-owned joint venture GBT Tokenize Corp. (GTC). Given the internal codename of smartGOAL, the platform is "an intelligent, automatic analytics and prediction system for soccer game's results," which works by analyzing and predicting "possible outcomes of soccer games results according to permutations, statistics, historical data, using advanced mathematical methods and machine learning technology." GBT's CTO, Danny Rittman, explained:

"Considering the popularity of the game in the present world, we believe organizations will be interested in prediction systems for the better performance of their teams. As interesting as it may seem, prediction of the results of a soccer game is a very hard task and involves a large amount of uncertainty. However, it can be said that the result of football is not a completely random event, and hence, we believe a few hidden patterns in the game can be utilized to potentially predict the outcome. Based on the studies of numerous researchers that are being reviewed in our study as well as those done in the previous years, one can say that with a sufficient amount of data an accurate prediction system can be built using various machine learning algorithms. While each algorithm has its advantages and disadvantages, a hybrid system that consists of more than one algorithm can be made with the goal of increasing the efficiency of the system as a whole. There also is a need for a comprehensive dataset through which better results can be obtained. Experts can work more toward gathering data related to different leagues and championships across the globe which may help in better understanding of the prediction system. Moreover, the distinctive characteristics of a soccer player, as well as that of the team, can also be taken into consideration while predicting as this may produce a better result as compared to when all the players in a game are treated to be having an equal effect on the game. The more information the system is trained with, we believe the more accurate the predictions and analysis will be. One of our joint venture companies, GTC, aimed to evaluate machine learning-driven applications in various fields, among them are entertainment, media and sports. We believe smartGOAL is an intelligent application that has the ability to change the world's soccer field when it comes to analytics and game score predictions."

Elsewhere, Amazon Web Services (AWS), a subsidiary of tech giant Amazon announced a collaboration with Maple Leaf Sports & Entertainment (MLSE), a sports and entertainment company that owns a host of Toronto-based sports franchises, to innovate the creation and delivery of "extraordinary sports moments and enhanced fan engagement." This will see MLSE utilize AWS AI, machine learning (ML), and deep learning cloud services to support their teams, lines of business, and how fans connect with each other and experience games. Humza Teherany, Chief Technology & Digital Officer at MLSE, commented:

"We built Digital Labs at MLSE to become the most technologically advanced organization in sport. As technology advances and how we watch and consume sports evolves, MLSE is dedicated to creating solutions and products that drive this evolution and elevate the fan experience. We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition. Taking a cloud-first approach to innovation with AWS further empowers our organization to experiment with new ideas that can help our teams perform their very best and our fans feel a closer connection to the action."

Similarly, IBM, the "Official Technology Partner of The [tennis] Championships for the past 33-years, has recently, alongside the All England Lawn Tennis Club, unveiled "new ways for Wimbledon fans around the world to experience The Championships digitally, powered by artificial intelligence (AI) running on IBM Cloud and hybrid cloud technologies." Kevin Farrar, Sports Partnership Leader, IBM UK & Ireland, explained:

"The digital fan features on the Wimbledon app and Wimbledon.com, beautifully designed by the IBM iX team and powered by AI and hybrid cloud technologies, are enabling the All England Club to immerse tennis lovers in the magic of The Championship, no matter where they are in the world. Sports fans love to debate and we're excited to introduce a new tool this year to enable that by allowing people to register their own match predictions and compare them with predictions generated by Match Insights with Watson and those of other fans."

Another firm cited in the Vantage Market Research report on AI in Sports was sports performance tech firm Catapult Group International Limited, who recently reported a multi-year deal with the German Football Association (DFB-Akademie) to "capture performance data via video, track athlete performance via wearables, and Improve the analysis infrastructure at all levels of the German National Football Teams." Will Lopes, CEO of Catapult, commented:

"We strive every day to unleash the potential of every athlete and team, and we're proud to partner with the prestigious German Football Association to fulfill that ambition. We're looking forward to partnering with the DFB to unlock what even the best coaches in the world cannot see on film or from the sidelines. This technology will empower athletes at all levels with data and insights to perform at their best."

With the seemingly inexorable tendency toward digitization in the presentation and analysis of sports, the accompanying use of AI-related technologies seems equally inevitable as is already borne out by current industry trends.

For a list of artificial intelligence stocks on Investorideas.com visit here.

About GBT Technologies Inc.

GBT Technologies, Inc. (OTC Pink: GTCH) ("GBT") (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT's mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT's goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT's vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

About Investorideas.com - News that Inspires Big Investing Ideas

Investorideas.com publishes breaking stock news, third party stock research, guest posts and original articles and podcasts in leading stock sectors. Learn about investing in stocks and get investor ideas in cannabis, crypto, AI and IoT, mining, sports biotech, water, renewable energy, gaming and more. Investor Idea's original branded content includes podcasts and columns : Crypto Corner , Play by Play sports and stock news , Investor Ideas Potcasts Cannabis News and Stocks on the Move podcast , Cleantech and Climate Change , Exploring Mining , Betting on Gaming Stocks Podcast and the AI Eye Podcast.

Disclaimer/Disclosure: Investorideas.com is a digital publisher of third party sourced news, articles and equity research as well as creates original content, including video, interviews and articles. Original content created by investorideas is protected by copyright laws other than syndication rights. Our site does not make recommendations for purchases or sale of stocks, services or products. Nothing on our sites should be construed as an offer or solicitation to buy or sell products or securities. All investing involves risk and possible losses. This site is currently compensated for news publication and distribution, social media and marketing, content creation and more. Disclosure is posted for each compensated news release, content published /created if required but otherwise the news was not compensated for and was published for the sole interest of our readers and followers. Contact management and IR of each company directly regarding specific questions. Disclosure: GTCH is a paid featured monthly AI stock on Investorideas.com More disclaimer info: https://www.investorideas.com/About/Disclaimer.asp Learn more about publishing your news release and our other news services on the Investorideas.com newswire https://www.investorideas.com/News-Upload/ Global investors must adhere to regulations of each country. Please read Investorideas.com privacy policy: https://www.investorideas.com/About/Private_Policy.asp

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Thu, 21 Jul 2022 23:10:00 -0500 en-US text/html https://finance.yahoo.com/news/ai-tech-stocks-growing-implementation-120000467.html
Killexams : Data-centric Security Market growth, business opportunities, share value, key insights and size estimation by 2027

"Informatica (US), IBM (US), Broadcom (US), Micro Focus (US), Varonis Systems (US), Talend (US), Orange Cyberdefense (France), Forcepoint (US), Imperva (US), NetApp (US), Infogix (US), PKWARE (US), Seclore (US), Fasoo (South Korea), Protegrity (US), Egnyte (US), Netwrix (US), Digital Guardian (US), HelpSystems (US), BigID (US), Securiti (US), SecuPi (US)."

Data-centric Security Market by Component (Software and Solutions and Professional Services), Deployment Mode (Cloud and On-premises), Organization Size, Vertical (Government and Defense, Healthcare, Telecommunications) Region - Global Forecast to 2027

MarketsandMarkets forecasts the global Data-centric Security Market size is projected to grow from an estimated value of USD 4.2 billion in 2022 to USD 12.3 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 23.9% from 2022 to 2027. Increasing demand for data-centric security solutions for cloud-based data security; stringent compliances and regulations; and increasing risk on enterprise data due to exploitation of big data analytics, machine learning, and artificial intelligence is driving the growth of data-centric security market across the globe.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1504980

By component, the services segment to register the highest growth rate during the forecast period

Based on component, data-centric security services have witnessed a growing demand in exact years. The services segment includes various services that are required to deploy, execute, and maintain data-centric security platforms in organizations. Enterprises need support from service providers to enhance their data management, policy control, auditing & reporting, data protection, and for maintaining the governance over various silos. Professional service providers help enterprises in deploying data centric security software and solutions and managing all the queries in the product life cycle.

The professional services are offered through professionals, specialists, or experts to support the business. These services include consulting, designing and development and implementation, and support services. The latest techniques, strategies, and skills adopted by the professionals are said to be helping organizations in adopting data centric security features. They also offer customized implementation and risk assessment and assist with the deployment via industry-defined best practices.

With the increasing demand for data-centric security solutions in high-growth markets such as APAC and MEA, there is a significant demand for training and education services to spread awareness about various data-centric security solutions.

Based on organization size, the SMEs segment to grow at the highest CAGR during the forecast period

By organization size, the data centric security market is sub-segmented into large enterprises and SMEs. SMEs use data centric security solutions to reduce data fraud while enhancing the customer experience. The growing usage of mobile devices has influenced the data transfer over business networks to personal devices, such as mobile phones and laptops. Hence, this helps in increasing the fraudulent data, cyberattacks, data losses, and threat of personal data thefts. These rising security issues have made way for SMEs to focus their concerns on data centric security. Although, SMEs have to consider their limited budget, the comprehension of corporate information being an important consideration, makes them use data discovery and classification, data protection, and data governance solutions. Moreover, these solutions are available at economical pricing in the cloud deployment type. In the coming years, data centric security solutions are expected to witness high adoption among SMEs, all over the region.

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What is data-centric security?

According to MarketsandMarkets, data-centric security is the process of protecting and shielding the data itself with the help of data discovery and classification, data protection and data governance, rather than protecting data containers, or system network, and perimeter security.

Competitive overview:

The data-centric security market is led by some of the globally established players such as Informatica (US), IBM (US), Broadcom (US), Micro Focus (US), Orange Cyberdefense (France), Forcepoint (US), and others. These players have adopted various growth strategies such as partnerships, agreements, collaboration, acquisitions, and product developments to increase their market presence.

IBM is renowned for its leading solutions in the data-centric security market. Today, IBM Cloud is known as the most open, secure, and reliable public cloud for business. IBM believes in leveraging data protection capabilities to secure data, achieve continuous security for enterprise applications and workloads with built-in isolation, access management, and integrated security posture.

IBM caters to a wide customer base spread globally. It is actively intensifying its position in the market through the organic strategy of new product launches. The company aims at satisfying its customers as per the changing market dynamics. This ability to innovate and respond to clients with correct information at the right time has enabled IBM to grow in the market. Therefore, IBM focuses on strengthening its position through strategic organic investments and acquisitions, broadening its technical expertise, and integrating AI with its offerings. It has transformed into a more agile enterprise to drive innovation and speed and help drive productivity that supports investments for participation in markets. The strong presence across the globe is one of the key factors leading to a good market share in the data-centric security market.

Apart from large and SME players, start-ups such as SecuPi, SealPath, Nucleus Cyber, Dathena, among others are also evolving in the data-centric security market space.

Another important player in the data-centric security market space is Broadcom which is a global technology company that develops semiconductor and infrastructure software solutions. In November 2019, Broadcom acquired Symantec’s Enterprise Security business for USD 10.7 billion. Broadcom’s products in the cybersecurity space include - Integrated Cyber Defense (ICD), endpoint security, network security, identity security, API security, and information security. Broadcom has a presence across Asia Pacific, the Americas, and EMEA countries. In the data-centric security market space, its Mainframe Compliance and Data Protection solution locates and protects sensitive and PII data to identify risk and ensure compliance. It also monitors system events to identify and proactively respond to potential risks and bad actors.

According to MnM’s market evaluation framework, most vendors in the data-centric security opt for inorganic growth strategies to maintain their competitive position in the market. The number of deals including partnerships, acquisitions, collaborations, and alliances increased by around 40-45% relatively in the period of 2020-2021. Due to the COVID-19 outbreak, there was a sudden decline in product launches, product enhancements, and business expansion activities. However, from 2022 onward, companies are expected to adopt organic growth strategies to provide data-centric security solutions and services to end users.

Browse Other reports:

Facility Management Market by Component (Solutions (IWMS, BIM, Facility Operations and Security Management) and Services), Deployment Mode (Cloud, On-premises), Organization Size, Vertical (BFSI, Retail), and Region (2022 - 2026)

IoT Security Market by Type (Network Security, Endpoint Security, Application Security, and Cloud Security), Component (Solutions & Services), Application Area, Deployment Mode (On-premises & Cloud), Organization Size, and Region (2022 - 2026)

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Killexams : EdTech and Smart Classrooms Market Analysis by Size, Share, Key Players, Growth, Trends & Forecast 2027

"Apple (US), Cisco (US), Blackboard (US), IBM (US), Dell EMC (US),Google (US), Microsoft (US), Oracle(US),SAP (Germany), Instructure(US)."

EdTech and Smart Classrooms Market by Hardware (Interactive Displays, Interactive Projectors), Education System Solution (LMS, TMS, DMS, SRS, Test Preparation, Learning & Gamification), Deployment Type, End User and Region - Global Forecast to 2027

MarketsandMarkets forecasts the global EdTech and Smart Classrooms Market to grow from USD 125.3 billion in 2022 to USD 232.9  billion by 2027, at a Compound Annual Growth Rate (CAGR) of 13.2% during the forecast period. The major factors driving the growth of the EdTech and smart classrooms market include increasing penetration of mobile devices and easy availability of internet, and growing demand for online teaching-learning models, impact of COVID-19 pandemic and growing need for EdTech solutions to keep education system running.

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=1066

Interactive Displays segment to hold the highest market size during the forecast period

Interactive displays helps to collaborate teaching with tech boost social learning. As per a study it has been discovered that frequent group activity in classrooms, often aided by technology, can result in 20% higher levels of social-emotional skill development. Students in these classes are also 13% more likely to feel confident contributing to class discussions. Interactive display encourages the real time collaboration. SMART Boards facilitate the necessary collaboration for students to develop these skills. Creating an audience response system on the interactive display allows students to use devices to participate in class surveys, quizzes, and games, and then analyse the results in real time. A large interactive whiteboard (IWB), also known as an interactive board or a smart board, is a large interactive display board in the shape of a whiteboard. It can be a standalone touchscreen computer used to perform tasks and operations on its own, or it can be a connectable apparatus used as a touchpad to control computers from a projector. They are used in a variety of settings, such as classrooms at all levels of education, corporate board rooms and work groups, professional sports coaching training rooms, broadcasting studios, and others.

Cloud deployment type to record the fastest growth rate during the forecast period

Technology innovation has provided numerous alternative solutions for businesses of all sizes to operate more efficiently. Cloud has emerged as a new trend in data centre administration. The cloud eliminates the costs of purchasing software and hardware, setting up and running data centres, such as electricity expenses for power and cooling of servers, and high-skilled IT resources for infrastructure management. Cloud services are available on demand and can be configured by a single person in a matter of minutes. Cloud provides dependability by storing multiple copies of data on different servers. The cloud is a potential technological creation that fosters change for its users. Cloud computing is an information technology paradigm that delivers computing services via the Internet by utilizing remote servers, database systems, networking, analytics, storage systems, software, and other digital facilities. Cloud computing has significant benefits for higher education, particularly for students transitioning from K-12 to university. Teachers can easily deliver online classes and engage their students in various programs and online projects by utilizing cloud technology in education. Cloud-based deployment refers to the hosted-type deployment of the game-based learning solution. There has been an upward trend in the deployment of the EdTech solution via cloud or dedicated data center infrastructure. The advantages of hosted deployment include reduced physical infrastructure, lower maintenance costs, 24×7 accessibility, and effective analysis of electronic business content. The cloud-based deployment of EdTech solution is crucial as it offers a flexible and scalable infrastructure to handle multiple devices and analyze ideas from employees, customers, and partners.

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Major EdTech and smart classrooms vendors include Apple (US), Cisco (US),  Blackboard (US), IBM (US), Dell EMC (US), Google (US), Microsoft (US), Oracle(US), SAP (Germany), Instructure(US). These market players have adopted various growth strategies, such as partnerships, agreements, and collaborations, and new product enhancements to expand their presence in the EdTech and smart classrooms market. Product enhancements and collaborations have been the most adopted strategies by major players from 2018 to 2020, which helped companies innovate their offerings and broaden their customer base.

A prominent player in the EdTech and smart classrooms market, Apple focuses on inorganic growth strategies such as partnerships, collaborations, and acquisitions. For instance, in August 2021 Apple launched Mobile Student ID through which students will be able to navigate campus and make purchases using mobile student IDs on the iPhone and Apple Watch. In July 2020 Apple partnered with HBCUs to offer innovative opportunities for coding to communities across the US. Apple deepened the partnership with an additional 10 HBCUs regional coding centers under its Community Education Initiative. The main objective of this partnership is to bring coding, creativity, and workforce development opportunities to learners of all ages. Apple offers software as well as hardware to empower educators with powerful products and tools. Apple offers several applications for K-12 education, including Schoolwork and Classroom. The company also offers AR in education to provide a better learning experience. Teaching tools helps to simplify teaching tasks with apps that make the classroom more flexible, collaborative, and personalized for each student. Apple has interactive guide that makes it easy to stay on task and organized while teaching remotely with iPad. The learning apps helps to manage schedules and screen time to minimize the distractions and also helps to create productive learning environments and make device set up easy for teachers and parents. Apple has various products, such as Macintosh, iPhone, iPad, wearables, and services. It has an intelligent software assistant named Siri, which has cloud-synchronized data with iCloud.

Blackboard has a vast product portfolio with diverse offerings across four divisions: K-12, higher education, government, and business. Under the K-12 division, the company offers products such as LMS, Synchronous Collaborative Learning, Learning Object Repository, Web Community Manager, Mass Notifications, Mobile Communications Application, Teacher Communication, Social Media Manager, and Blackboard Ally. Its solutions include Blackboard Classroom, Collaborate Starter, and Personalized Learning. Blackboard’s higher education division products include Blackboard Learn, Blackboard Collaborate, Analytics for Learn, Blackboard Intelligence, Blackboard Predict, Outcomes and Assessments, X-ray for Learning Analytics, Blackboard Connect, Blackboard Instructor, Moodlerooms, Blackboard Transact, Blackboard Ally, and Blackboard Open Content. The company also provides services, such as student pathway services, marketing, and recruiting, help desk services, enrollment management, financial aid and student services, engagement campaigns, student retention, training and implementation services, strategic consulting, and analytics consulting services. Its teaching and learning solutions include LMS, education analytics, web conferencing, mobile learning, open-source learning, training and implementation, virtual classroom, and competency-based education. Blackboard also offers campus enablement solutions such as payment solutions, security solutions, campus store solutions, and transaction solutions. Under the government division, it offers solutions such as LMS, registration and reporting, accessibility, collaboration and web conferencing, mass notifications and implementation, and strategic consulting. The company has launched Blackboard Unite on April 2020 for K-12. This solution compromises a virtual classroom, learning management system, accessibility tool, mobile app, and services and implementation kit to help emote learning efforts.

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Killexams : IBM Aims to Capture Growing Market Opportunity for Data Observability with Databand.ai Acquisition No result found, try new keyword!Extends IBM's leadership in observability to the full stack of capabilities for IT -- across infrastructure, applications, data and machine learning Data observability takes traditional data ... Wed, 06 Jul 2022 00:37:00 -0500 https://news.webindia123.com/news/press_showdetailsPR.asp?id=1264459&cat=PR%20News%20Wire Killexams : AI Tech Stocks and the Growing Implementation in the Sports Market

Vancouver, Kelowna and Delta, British Columbia--(Newsfile Corp. - July 21, 2022) - Investorideas.com (www.investorideas.com), a global investor news source covering Artificial Intelligence (AI) stocks releases a sector snapshot looking at the growing AI tech implementation in the sports market, featuring AI innovator GBT Technologies Inc. (OTC Pink: GTCH).

Read the full article at Investorideas.com

As with so many other sectors, the sports industry is seeing increasing penetration of Artificial Intelligence (AI) related technologies as aspects of the medium become more and more digitized. A recently published report from Vantage Market Research finds that the global market for AI in Sports is projected to grow from $1.62 billion USD in 2021 to $7.75 billion by 2028, registering a compound annual growth rate (CAGR) of 29.7 percent in the forecast period 2022-28. According to a market synopsis from the report, AI is being leveraged by a number of firms to track player performance, Improve the player's health, and to Improve sports planning.

One such firm is GBT Technologies Inc. (OTC Pink: GTCH), an early stage technology developer in IoT and Artificial Intelligence (AI) Enabled Mobile Technology Platforms, which recently completed phase one of its intelligent soccer analytics platform through its 50 percent-owned joint venture GBT Tokenize Corp. (GTC). Given the internal codename of smartGOAL, the platform is "an intelligent, automatic analytics and prediction system for soccer game's results," which works by analyzing and predicting "possible outcomes of soccer games results according to permutations, statistics, historical data, using advanced mathematical methods and machine learning technology." GBT's CTO, Danny Rittman, explained:

"Considering the popularity of the game in the present world, we believe organizations will be interested in prediction systems for the better performance of their teams. As interesting as it may seem, prediction of the results of a soccer game is a very hard task and involves a large amount of uncertainty. However, it can be said that the result of football is not a completely random event, and hence, we believe a few hidden patterns in the game can be utilized to potentially predict the outcome. Based on the studies of numerous researchers that are being reviewed in our study as well as those done in the previous years, one can say that with a sufficient amount of data an accurate prediction system can be built using various machine learning algorithms. While each algorithm has its advantages and disadvantages, a hybrid system that consists of more than one algorithm can be made with the goal of increasing the efficiency of the system as a whole. There also is a need for a comprehensive dataset through which better results can be obtained. Experts can work more toward gathering data related to different leagues and championships across the globe which may help in better understanding of the prediction system. Moreover, the distinctive characteristics of a soccer player, as well as that of the team, can also be taken into consideration while predicting as this may produce a better result as compared to when all the players in a game are treated to be having an equal effect on the game. The more information the system is trained with, we believe the more accurate the predictions and analysis will be. One of our joint venture companies, GTC, aimed to evaluate machine learning-driven applications in various fields, among them are entertainment, media and sports. We believe smartGOAL is an intelligent application that has the ability to change the world's soccer field when it comes to analytics and game score predictions."

Elsewhere, Amazon Web Services (AWS), a subsidiary of tech giant Amazon announced a collaboration with Maple Leaf Sports & Entertainment (MLSE), a sports and entertainment company that owns a host of Toronto-based sports franchises, to innovate the creation and delivery of "extraordinary sports moments and enhanced fan engagement." This will see MLSE utilize AWS AI, machine learning (ML), and deep learning cloud services to support their teams, lines of business, and how fans connect with each other and experience games. Humza Teherany, Chief Technology & Digital Officer at MLSE, commented:

"We built Digital Labs at MLSE to become the most technologically advanced organization in sport. As technology advances and how we watch and consume sports evolves, MLSE is dedicated to creating solutions and products that drive this evolution and elevate the fan experience. We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition. Taking a cloud-first approach to innovation with AWS further empowers our organization to experiment with new ideas that can help our teams perform their very best and our fans feel a closer connection to the action."

Similarly, IBM, the "Official Technology Partner of The [tennis] Championships for the past 33-years, has recently, alongside the All England Lawn Tennis Club, unveiled "new ways for Wimbledon fans around the world to experience The Championships digitally, powered by artificial intelligence (AI) running on IBM Cloud and hybrid cloud technologies." Kevin Farrar, Sports Partnership Leader, IBM UK & Ireland, explained:

"The digital fan features on the Wimbledon app and Wimbledon.com, beautifully designed by the IBM iX team and powered by AI and hybrid cloud technologies, are enabling the All England Club to immerse tennis lovers in the magic of The Championship, no matter where they are in the world. Sports fans love to debate and we're excited to introduce a new tool this year to enable that by allowing people to register their own match predictions and compare them with predictions generated by Match Insights with Watson and those of other fans."

Another firm cited in the Vantage Market Research report on AI in Sports was sports performance tech firm Catapult Group International Limited, who recently reported a multi-year deal with the German Football Association (DFB-Akademie) to "capture performance data via video, track athlete performance via wearables, and Improve the analysis infrastructure at all levels of the German National Football Teams." Will Lopes, CEO of Catapult, commented:

"We strive every day to unleash the potential of every athlete and team, and we're proud to partner with the prestigious German Football Association to fulfill that ambition. We're looking forward to partnering with the DFB to unlock what even the best coaches in the world cannot see on film or from the sidelines. This technology will empower athletes at all levels with data and insights to perform at their best."

With the seemingly inexorable tendency toward digitization in the presentation and analysis of sports, the accompanying use of AI-related technologies seems equally inevitable as is already borne out by current industry trends.

For a list of artificial intelligence stocks on Investorideas.com visit here.

About GBT Technologies Inc.

GBT Technologies, Inc. (OTC Pink: GTCH) ("GBT") (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT's mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT's goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT's vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.

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Investorideas.com publishes breaking stock news, third party stock research, guest posts and original articles and podcasts in leading stock sectors. Learn about investing in stocks and get investor ideas in cannabis, crypto, AI and IoT, mining, sports biotech, water, renewable energy, gaming and more. Investor Idea's original branded content includes podcasts and columns : Crypto Corner , Play by Play sports and stock news , Investor Ideas Potcasts Cannabis News and Stocks on the Move podcast , Cleantech and Climate Change , Exploring Mining , Betting on Gaming Stocks Podcast and the AI Eye Podcast.

Disclaimer/Disclosure: Investorideas.com is a digital publisher of third party sourced news, articles and equity research as well as creates original content, including video, interviews and articles. Original content created by investorideas is protected by copyright laws other than syndication rights. Our site does not make recommendations for purchases or sale of stocks, services or products. Nothing on our sites should be construed as an offer or solicitation to buy or sell products or securities. All investing involves risk and possible losses. This site is currently compensated for news publication and distribution, social media and marketing, content creation and more. Disclosure is posted for each compensated news release, content published /created if required but otherwise the news was not compensated for and was published for the sole interest of our readers and followers. Contact management and IR of each company directly regarding specific questions. Disclosure: GTCH is a paid featured monthly AI stock on Investorideas.com More disclaimer info: https://www.investorideas.com/About/Disclaimer.asp Learn more about publishing your news release and our other news services on the Investorideas.com newswire https://www.investorideas.com/News-Upload/ Global investors must adhere to regulations of each country. Please read Investorideas.com privacy policy: https://www.investorideas.com/About/Private_Policy.asp

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