How Not to Make Backups – The Union Journal

The mantra about the crucial role of data backups in digital security has some solid reasoning behind it. Not only is this a way to minimize the damage in a hardware failure scenario, but its also a fundamental element of mitigating the impact of a ransomware attack. This issue has escalated amid todays global healthcare emergency because cybercriminals are busier than ever orchestrating Coronavirus-themed phishing and spam campaigns that parasitize peoples fears to spread ransom Trojans on a large scale.

Organizations are predictably the juiciest prey being hunted down in ransomware raids. Moreover, malefactors continue to target hospitals in these hard times, as if the challenge tackling the COVID-19 outbreak werent arduous enough for these facilities. The dramatic increase in telework is an extra stimulus for crooks to find and exploit loopholes in VPN tools and cloud services used for remote workplace implementation.

With that said, maintaining backups of the most valuable data assets is growingly important for individuals and businesses alike. However, it turns out that a crudely configured backup can do your company a disservice instead of strengthening its security posture. If you are curious about how this could possibly be the case, keep reading to learn the whys and wherefores.

The wakeup call

According to recent findings of security researchers, an incorrectly implemented data backup poses an opportunity for an adversary to amass an organizations valuable files the easy way, no matter how counterintuitive it may sound. Before I proceed, its worth clarifying a few things to give you an idea of the current state of the ransomware ecosystem.

A game-changing trend in this context is that some attackers now steal victims data prior to encrypting it. Several examples of the ransomware families that employ this tactic are Sodinokibi, Maze, DoppelPaymer, and Nemty. Once the criminals retrieve data, they use it as additional leverage to coerce the victim into paying the ransom. If a company refuses to cough up the specified amount of Bitcoin, ransomware operators switch to plan B and publish sensitive information for everyone to see.

Essentially, the attack isnt only about malicious encryption anymore its also about the risk of data breaches and huge reputational damages. To top it off, some cybercriminal groups have launched special websites where they leak the data stolen from non-paying businesses.

You might be wondering what this narrative has to do with backups well, the ties are closer than you probably think. The threat actors behind the above-mentioned DoppelPaymer ransomware recently updated their leak site with an entry listing credentials for the Veeam backup solution used by one of the compromised organizations.

Analysts at Bleeping Computer security outlet who looked into the incident argue that the attackers intention wasnt to punish the organization for rejecting the ransom demands. Instead, it was proof of unlimited access to the victims digital infrastructure, including backups. This way, the felons tried to pressure the company into paying up.

To dot the is and cross the ts, the researchers tried to contact the operators of two very active ransomware strains, DoppelPaymer and Maze, and ask them about this facet of their nefarious activity. On a side note, the experts had previously communicated with these black hats who didnt mind explaining some of their tactics, techniques, and procedures (TTP). The perpetrators response to this particular matter was very surprising.

The cybercrooks described their common attack chain and the role of data backups in it. First, they contaminate a single machine on a network through phishing, auxiliary malware, or remote desktop protocol (RDP) exploitation. As soon as the computer is infiltrated, the offenders move laterally across the network in an attempt to get hold of admin credentials and access the domain controller.

If the attackers succeed in gaining a foothold in the enterprise environment, they leverage a post-exploitation application such as Mimikatz to dump the entirety of authentication data from the active directory database. The consequences of this activity can be hugely disruptive because the obtained information may allow the malefactors to access backup tools used by the organization. The likelihood of this adverse effect is higher if network admins use Windows session authentication to log in to Veeam or another mainstream backup software.

From there, ransomware operators can easily access the victimized companys cloud backups and download all the data to a malicious server. This way, they take a shortcut because there is no need for them to traverse the whole corporate network in search of potentially valuable information cloud backups typically contain the data that matters the most.

An extra benefit for malicious actors who take this route is that the data theft slips below the radar of automated defenses deployed in the network. Restoring directly from the cloud doesnt give IT teams a heads-up because the servers appear to be functioning properly and the backup software doesnt trigger any alerts either.

Once the attackers download all the important files, they delete the backups to prevent the victim from easily recovering from the incursion. Then, they launch the PSExec command-line utility to unleash the ransomware that will encrypt the organizations data surreptitiously.

At the end of the day, although backups are a critical element of incident response, they can be used against companies unless set up properly. Ransomware distributors piggyback on poor backup hygiene to steal data faster without any red flags being raised along the way. This negligence can fuel the extortionists novel strategy thats increasingly capitalizing on data theft before encryption. Offline backups appear to be more effective in this regard, but they are often outdated.

Luckily, there are methods that can help businesses boost their protection against this exploitation vector and make the attackers efforts futile. The fundamental countermeasure is the so-called 3-2-1 rule. It eliminates the risk of a single point of failure (SPOF) in case hardware crashes or a strain of ransomware poisons the enterprise network. In a nutshell, the logic of this mechanism is as follows: store at least three copies of your valuable data, keep two of them on different storage media, and be sure to store one backup copy offline.

The types of storage media for this diversified backup approach can range from external hard disks or USB thumb drives to SD cards or CDs/DVDs. The choice depends on the amount of data to be kept safe. Prioritizing your information is a worthwhile element of facilitating this activity because it narrows down the scope of data to the items that really matter. When it comes to offline backups, its important to ascertain that they hold the latest versions of your files.

If you adhere to the 3-2-1 principle, there is little to no risk of losing your precious data over a ransomware incident, hardware malfunctions, or things like the vengeance of a disgruntled employee. Essentially, it helps your organization steer clear of the worst-case scenario, making your security posture resilient to a disaster no matter where it may come from.

Experts additionally recommend that businesses resort to whats called immutable storage to further enhance their data integrity. This technique makes it impossible to erase or modify backups for a specified period of time.

Furthermore, the saying prevention is the best cure has never been as relevant as it is nowadays. To defend against ransomware attacks and data breaches proactively, organizations should deploy network monitoring tools, cloud access control instruments based on IP addresses and geolocation, and intrusion detection systems (IDS). This combo will stop criminals in their tracks and save companies the trouble of dealing with the mind-boggling aftermath of a compromise.

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The coronavirus contact tracing app won’t log your location, but it will reveal who you hang out with – The Conversation AU

The federal government has announced plans to introduce a contact tracing mobile app to help curb COVID-19s spread in Australia.

Read more: Explainer: what is contact tracing and how does it help limit the coronavirus spread?

However, rather than collecting location data directly from mobile operators, the proposed TraceTogether app will use Bluetooth technology to sense whether users who have voluntarily opted-in have come within nine metres of one another.

Contact tracing apps generally store 14-21 days of interaction data between participating devices to help monitor the spread of a disease. The tracking is usually done by government agencies. This form of health surveillance could help the Australian government respond to the coronavirus crisis by proactively placing confirmed and suspected cases in quarantine.

The TraceTogether app has been available in Singapore since March 20, and its reception there may help shed light on how the new tech will fare in Australia.

Read more: Privacy vs pandemic: government tracking of mobile phones could be a potent weapon against COVID-19

Internationally, contact tracing is being explored as a key means of containing the spread of COVID-19. The World Health Organization (WHO) identifies three basic steps to any form of contact tracing: contact identification, contact listing, and follow-up.

Contact identification records the mobile phone number and a random anonymised user ID. Contact listing includes a record of users who have come into close contact with a confirmed case, and notifies them of next steps such as self-isolation. Finally, follow-up entails frequent communication with contacts to monitor the emergence of any symptoms and test accordingly to confirm.

The TraceTogether app has been presented as a tool to protect individuals, families and society at large through a community data-driven approach. Details on proximity and contact duration are shared between devices that have the app installed. An estimated 17% of Singapores population has done this.

In an effort to preserve privacy, the apps developers claim it retains proximity and duration details for 21 days, after which the oldest days record is deleted and the latest days data is added.

Read more: Tracking your location and targeted texts: how sharing your data could help in New Zealand's level 4 lockdown

TraceTogether supposedly doesnt collect users location data thereby mitigating concerns about location privacy usually linked to such apps. But proximity and duration information can reveal a great deal about a users relative distance, time and duration of contact. A bluetooth-based app may not know where you are on Earths surface, but it can accurately infer your location when bringing a variety of data together.

The introduction of a contact tracing app in Australia will allow health authorities to alert community members who have been in contact with a confirmed case of COVID-19.

However, as downloading the app is voluntary, its effectiveness relies on an uptake from a certain percentage of Australians - specifically 40%, according to an ABC report.

But this proposed model overlooks several factors. First, it doesnt account for accessibility by vulnerable individuals who may not own or be able to operate a smartphone, potentially including the elderly or those living with cognitive impairment. Also, its presently unclear whether privacy and security issues have been or will be integrated into the functional design of the system when used in Australia.

This contact tracing model is also not open source software, and as such is not subject to audit or oversight. As it has currently been deployed in Singapore, it also places a government authority in control of the transfer of valuable contact and connection details. The question is now how these systems will stack up against corporate implementations like that being proposed by Google and Apple.

Also, those who criticise contact tracing point out that the technology is after the fact when it is too late, rather than preventive in nature, although it might act to lower transmission rates. Some research has proposed a more preemptive approach, location intelligence, implemented by responsible artificial intelligence, to predict (and respond to) how an outbreak might play out.

Others argue that if were all self-isolating, there should be no need for unproven technology, and that attention may instead be focused on digital immunity certificates, allowing some people to roam while others do not.

And in the apps created to respond to particular situations, theres always the question of: who owns the data?. A pandemic-tracing app would need to have a limited lifetime, even if the user forgets to uninstall the COVID-19 app after victory has been declared over the pandemic. It must not become the de facto operational scenario this would have major societal ramifications.

In the end, it may simply come down to trust. Do Australians trust their data in the hands of the government? The answer might well be no, but do we have any other choice?

Or for that matter what about data in the hands of corporations? Time and time again, government and corporates have failed to conduct adequate impact assessments, have been in breach of their own laws, regulations, policies and principles, have systems at scale that have suffered from scope and function creep, and have used data retrospectively in ways that were never intended. But is this the time for technology in the public interest to proliferate through the adoption of emerging technologies?

No one fears tech for good. But we must not relax fundamental requirements of privacy, strategies for maintaining anonymity, the encryption of data, and preventing our information from landing in the wrong hands. We need to ask ourselves, can we do better and what provisions are in place to maintain our civil liberties while at the same time remaining secure and safe?

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The coronavirus contact tracing app won't log your location, but it will reveal who you hang out with - The Conversation AU

Teslas acquisition of DeepScale starts to pay off with new IP in machine learning – Electrek

Teslas acquisition of machine-learning startup DeepScale is starting to pay off, with the team hired through the acquisition starting to deliver new IP for the automaker.

Late last year, it was revealed that Tesla acquired DeepScale, a Bay Area-based startup that focuses on Deep Neural Network (DNN) for self-driving vehicles, for an undisclosed amount.

They specialized in computing power-efficient deep learning systems, which is also an area of focus for Tesla, who decided to design its own computer chip to power its self-driving software.

There was speculation that Tesla acquired the small startup team in order to accelerate its machine learning development.

Now we are seeing some of that teams work, thanks to a new patent application.

Just days after Tesla acquired the startup in October 2019, the automaker applied for a new patent with three members of DeepScale listed as inventors: Matthew Cooper, Paras Jain, and Harsimran Singh Sidhu.

The patent application called Systems and Methods for Training Machine Models with Augmented Data was published yesterday.

Tesla writes about it in the application:

Systems and methods for training machine models with augmented data. An example method includes identifying a set of images captured by a set of cameras while affixed to one or more image collection systems. For each image in the set of images, a training output for the image is identified. For one or more images in the set of images, an augmented image for a set of augmented images is generated. Generating an augmented image includes modifying the image with an image manipulation function that maintains camera properties of the image. The augmented training image is associated with the training output of the image. A set of parameters of the predictive computer model are trained to predict the training output based on an image training set including the images and the set of augmented images.

The system that the DeepScale team, now working under Tesla, is trying to patent here is related to training a neural net using data from several different sensors observing scenes, like the eight cameras in Teslas Autopilot sensor array.

They write about the difficulties of such a situation in the patent application:

In typical machine learning applications, data may be augmented in various ways to avoid overfitting the model to the characteristics of the capture equipment used to obtain the training data. For example, in typical sets of images used for training computer models, the images may represent objects captured with many different capture environments having varying sensor characteristics with respect to the objects being captured. For example, such images may be captured by various sensor characteristics, such as various scales (e.g., significantly different distances within the image), with various focal lengths, by various lens types, with various pre- or post-processing, different software environments, sensor array hardware, and so forth. These sensors may also differ with respect to different extrinsic parameters, such as the position and orientation of the imaging sensors with respect to the environment as the image is captured. All of these different types of sensor characteristics can cause the captured images to present differently and variously throughout the different images in the image set and make it more difficult to properly train a computer model.

Here they summarize their solution to the problem:

One embodiment is a method for training a set of parameters of a predictive computer model. This embodiment may include: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

An additional embodiment may include a system having one or more processors and non-transitory computer storage media storing instructions that when executed by the one or more processors, cause the processors to perform operations comprising: identifying a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identifying a training output for the image; for one or more images in the set of images, generating an augmented image for a set of augmented images by: generating an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associating the augmented training image with the training output of the image; training the set of parameters of the predictive computer model to predict the training output based on an image training set including the images and the set of augmented images.

Another embodiment may include a non-transitory computer-readable medium having instructions for execution by a processor, the instructions when executed by the processor causing the processor to: identify a set of images captured by a set of cameras while affixed to one or more image collection systems; for each image in the set of images, identify a training output for the image; for one or more images in the set of images, generate an augmented image for a set of augmented images by: generate an augmented image for a set of augmented images by modifying the image with an image manipulation function that maintains camera properties of the image, and associate the augmented training image with the training output of the image; train the computer model to learn to predict the training output based on an image training set including the images and the set of augmented images.

As we previously reported, Tesla is going through a significant foundational rewrite in the Tesla Autopilot. As part of the rewrite, CEO Elon Musk says that the neural net is absorbing more and more of the problem.

It will also include a more in-depth labeling system.

Musk described 3D labeling as a game-changer:

Its where the car goes into a scene with eight cameras, and kind of paint a path, and then you can label that path in 3D.

This new way to train machine learning systems with multiple cameras, like Teslas Autopilot, with augmented data could be part of this new Autopilot update.

Here are some drawings from the patent application:

Heres Teslas patent application in full:

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Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more – OnMSFT

Welcome back to our Windows 10 news recap, where we go over the top stories of the past week in the world of Microsofts flagship operating system.

Microsoft to introduce PowerToys launcher for Windows 10 in May

A new report suggests that a new update for PowerToys is being prepared that includes a Mac OS style Spotlight launcher, making it easier find apps and files on a Windows 10 PC.

concept design for PowerToys Launcher UX

Microsoft starts sending invites for first Halo 2 Anniversary beta on PC

Invites for the Halo 2 Anniversary beta on PC have started to be sent out this week. Members of the Halo Insider program who have opted into PC flighting will receive an email with the invite.

Microsoft is using machine learning to identify security bugs during software development

In order to help Microsoft identify security bugs and resolve them before public release of software, the company is employing machine learning to find security bugs.

Thats it for this week. Well be back next week with more Windows 10 news.

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Windows 10 news recap: Halo 2 Anniversary beta invites being sent out, machine learning utilised to identify security bugs, and more - OnMSFT

Podcast of the Week: TWIML AI Podcast – 9to5Mac

During the COVID19 pandemic, I decided that I wanted to use the time at home to invest in myself. One of the things I was challenged by in a recent episode of Business Casual was when Mark Cuban discussed the role of Artificial Intelligence in the future and recommended some tools to learn more. He mentioned some Coursera courses, so I am currently working my way through some of their AI training, but he also mentioned an AI-focused podcast called theTWIMLAI Podcast that I added to my podcast subscription list.

9to5Macs Podcast of the Week is a weekly recommendation of a podcast you should add to your subscription list.

TWIML (This Week in Machine Learning and AI) is a perfect way to hear from industry experts about how Machine Learning and AI will change our world. I plan to work through the back catalog soon, but the newest episodes have been informative. I particularly enjoyed this episode with Cathy Wu, Gilbert W. Winslow Career Development Assistant Professor in the Department of Civil and Environmental Engineering at MIT where they discussed simulating the future of traffic.

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. By sharing and amplifying the voices of a broad and diverse spectrum of machine learning and AI researchers, practitioners, and innovators, our programs help make ML and AI more accessible, and enhance the lives of our audience and their communities.

TWIML has its origins in This Week in Machine Learning & AI, a podcast Sam launched in mid2016 to a small but enthusiastic reception. Fast forward three years, and the TWIML AI Podcast is now a leading voice in the field, with over five million downloads and a large and engaged community following. Our offerings now include online meetups and study groups, conferences, and a variety of educational content.

Subscribe to the TWIML AI Podcast on Apple Podcasts, Spotify, Castro, Overcast, Pocket Casts, and RSS.

Dont forget about the great lineup of podcasts on the 9to5 Network.

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SAP Added to its Next-Generation Support – ARC Advisory Group

SAP SE announced several updates, including the Schedule a Manager and Ask an Expert Peer services, to its Next-Generation Support approach focused on the customer support experience and enabling customer success.

Based on artificial intelligence (AI) and machine learning technologies, SAP has further developed existing functionalities with new, automated capabilities, such as the Incident Solution Matching service and automatic translation.

AI and machine learning technologies are key to improving and simplifying the customer support experience. They continue to play an important role in expanding Next-Generation Support to help SAP deliver maximum business outcomes for customers. SAP has expanded its offerings by adding new features to existing services, for example:

Customers expect their issues to be resolved quickly, and SAP strives toward a consistent line of communication across all support channels, including real-time options. SAP continues to improve, innovate and extend live support for technical issues by connecting directly with customers to provide a personal customer experience. Building on top of live support services, such as Expert Chat and Schedule an Expert, SAP has made significant strides in upgrading its real-time support channels. For example, it now offers the Schedule a Manager service and a peer-to-peer collaboration channel through the Ask an Expert Peer service.

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SAP Added to its Next-Generation Support - ARC Advisory Group

Machine Learning as a Service (MLaaS) Market | Outlook and Opportunities in Grooming Regions with Forecast to 2029 – Jewish Life News

Documenting the Industry Development of Machine Learning as a Service (MLaaS) Market concentrating on the industry that holds a massive market share 2020 both concerning volume and value With top countries data, Manufacturers, Suppliers, In-depth research on market dynamics, export research report and forecast to 2029

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***[Note: Our Complimentary Sample Report Accommodate a Brief Introduction To The Synopsis, TOC, List of Tables and Figures, Competitive Landscape and Geographic Segmentation, Innovation and Future Developments Based on Research Methodology are also Included]

An Evaluation of the Machine Learning as a Service (MLaaS) Market:

The report is a detailed competitive outlook including the Machine Learning as a Service (MLaaS) Market updates, future growth, business prospects, forthcoming developments and future investments by forecast to 2029. The region-wise analysis of machine learning as a service (mlaas) market is done in the report that covers revenue, volume, size, value, and such valuable data. The report mentions a brief overview of the manufacturer base of this industry, which is comprised of companies such as- Google, IBM Corporation, Microsoft Corporation, Amazon Web Services, BigML, FICO, Yottamine Analytics, Ersatz Labs, Predictron Labs, H2O.ai, AT and T, Sift Science.

Segmentation Overview:

Product Type Segmentation :

Software Tools, Cloud and Web-based Application Programming Interface (APIs), Other

Application Segmentation :

Manufacturing, Retail, Healthcare and Life Sciences, Telecom, BFSI, Other (Energy and Utilities, Education, Government)

To know more about how the report uncovers exhaustive insights |Enquire Here: https://market.us/report/machine-learning-as-a-service-mlaas-market/#inquiry

Key Highlights of the Machine Learning as a Service (MLaaS) Market:

The fundamental details related to Machine Learning as a Service (MLaaS) industry like the product definition, product segmentation, price, a variety of statements, demand and supply statistics are covered in this article.

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The important tactics of top players in the market.

Other points comprised in the Machine Learning as a Service (MLaaS) report are driving factors, limiting factors, new upcoming opportunities, encountered challenges, technological advancements, flourishing segments, and major trends of the market.

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Machine Learning as a Service (MLaaS) Market | Outlook and Opportunities in Grooming Regions with Forecast to 2029 - Jewish Life News

2020 AI Zest Automated Machine Learning Market Opportunities, Analysis, Future and Forecast by Key Players, Products, Types and Applications – Germany…

AI Zest Automated Machine Learning:

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This report studies the AI Zest Automated Machine Learning market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the AI Zest Automated Machine Learning market by product type and applications/end industries.

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The segment analysis is one of the significant sections of this report. Our expert analyst has categorized the market into product type, application/end-user, and geography. All the segments are analyzed based on their market share, growth rate, and growth potential. In the geographical classification, the report highlights the regional markets having high growth potential. This thorough evaluation of the segments would help the players to focus on revenue-generating areas of the Vertical Farming market.

Regional Analysis

Our analysts are experts in covering all types of geographical markets from developing to mature ones. You can expect a comprehensive research analysis of key regional and country-level markets such as Europe, North America, South America, Asia-Pacific, and the Middle East & Africa. With accurate statistical patterns and regional classification, our domain experts provide you one of the most detailed and easily understandable regional analyses of the AI Zest Automated Machine Learning market.

Competitive Landscape:

The research report also studied the key players operating in the AI Zest Automated Machine Learning market. It has evaluated and explained the research & development stages of these companies, their financial performances, and their expansion plans for the coming years. Moreover, the research report also includes the list of planned initiatives that clearly explain the accomplishments of the companies in the recent past.

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The research methodology of the market is based on both primary as well as secondary research data sources. It compels different factors affecting the AI Zest Automated Machine Learning industry such as historical data and market trends, different policies of the government, market environment, market risk factors, market restraints, technological advancements, forthcoming innovations, and obstacles in the industry.

The content of the study subjects includes a total of 8 chapters:

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2020 AI Zest Automated Machine Learning Market Opportunities, Analysis, Future and Forecast by Key Players, Products, Types and Applications - Germany...

Machine Learning as a Service Market: Which business strategy will be prominent? – The Canton Independent Sentinel

The Machine Learning as a Service (MLaaS) market research report study recently presented by AMR provides comprehensive knowledge on the development activities by Global industry players, growth possibilities or opportunities and market sizing for Machine Learning as a Service (MLaaS) along with analysis by key segments, leading and emerging players, and their presence geographies.

This research study has 196 pages, it covers the complete market overview of various profiled players and their development history, on-going development strategies along with the current situation.

In this report, we analyze the Machine Learning as a Service (MLaaS) industry from two aspects. One part is about its production and the other part is about its consumption. In terms of its production, we analyze the production, revenue, gross margin of its main manufacturers and the unit price that they offer in different regions from 2014 to 2019. In terms of its consumption, we analyze the consumption volume, consumption value, sale price, import and export in different regions from 2014 to 2019. We also make a prediction of its production and consumption in coming 2019-2024.

The research benefits in recognizing and following arising players in the market and their portfolios, to enhance decision-making abilities and helps to create effective counter-strategies to gain a competing advantage. Some of the players profiled/ part of study coverage are Microsoft, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T.

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AMRs research team has examined complete data across the globe comprising 20+ countries with a comprehensive data plan spread from 2013 to 2026 and approximately 12+ regional indicators complemented with 20+ company level coverage.

The study is organized utilizing data and knowledge sourced of various primary and secondary sources, proprietary databases, company/university websites, regulators, conferences, SEC filings, investor presentations and featured press releases from company sites and industry-specific third party sources.

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Characteristics of the Table of Content:

The comprehensive study presented by considering all the important aspects and sections. Some of these were

Machine Learning as a Service (MLaaS) MARKET RESEARCH SCOPE OBJECTIVES, TARGET AND KEY FINDINGS

Preferably, that approaching major uptrend failed to arrive on schedule, but the Machine Learning as a Service (MLaaS) market raised without posting any drops and surely witnesses zeniths in years to come.

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Banking, Financial Services, Insurance, Automobile, Health Care, Defense, Retail, Media & Entertainment, Communication segment interpreted and sized in this research report by application/end-users reveals the inherent growth and several shifts for the period 2014 to 2026.

The changing dynamics supporting the growth perform it perilous for manufacturers in this extent to keep up-to-date with the changing pace of the market. Find out which segment is doing great and will return in strong earnings adding the significant drive to overall growth.

Furthermore, the research contributes an in-depth overview of regional level break-up categorized as likely leading growth rate territory, countries with the highest market share in past and current scenario. Some of the geographical break-up incorporated in the study are North America, Europe, Asia Pacific, Middle East & Africa, Latin America.

In the Type segment Special Service, Management Services included for segmenting Machine Learning as a Service (MLaaS) market by type.

The industry is performing well and few emerging business institutions are in their peak as per growth rate and their existence with major players of Machine Learning as a Service (MLaaS) market whereas conflict between 2 Global economies continues in 2020.

Microsoft, International Business Machine, Amazon Web Services, Google, Bigml, Fico, Hewlett-Packard Enterprise Development, At&T major key players included in this research along with their sales and revenue data show how they are performing well?

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Machine Learning as a Service Market: Which business strategy will be prominent? - The Canton Independent Sentinel

Ripple Poised to Sell XRP for 21 More Years Heres How Much Cryptocurrency the Payments Startup Has Sold So Far – The Daily Hodl

The San Francisco-based cross-border payments company Ripple is set to continue selling XRP for the next 21 years.

Ripple owns more than half of the total supply of 100 billion XRP and set up an escrow program in 2017 to manage its sales.

According to a new report from XRPArcade, an independent media source covering the cryptocurrency, Ripple has sold an average of 196 million XRP per month since December of 2017. At that rate, the company will continue its sales until April of 2041.

A total of 5.5 billion XRP has permanently left Ripples escrow wallets, which indicates the company has sold a total of $1.03 billion worth of XRP at time of publishing.

A known Ripple wallet that is used to distribute XRP to third parties has remained highly active and sent 75,202,210 XRP to wallets of unknown origin in February.

Ripple, which is set to release its XRP Markets Report for the first quarter of 2020 by the end of April, has slowed its sales of the third-largest cryptocurrency in recent months.

In the fourth quarter of last year, the company says it sold 13.08 million XRP directly to institutional players in over-the-counter transactions. The company did not sell any XRP on crypto exchanges.

A Ripple-owned wallet used exclusively for selling its holdings to institutions, however, has remained highly active in the month of February, sending 75,202,210 XRP worth $17 million to wallets of unknown origin.

Ripple will reveal the exact amount of XRP sold when the company releases its first quarter recap on its movements of the crypto asset.

Featured Image: Shutterstock/coloursinmylife

Excerpt from:
Ripple Poised to Sell XRP for 21 More Years Heres How Much Cryptocurrency the Payments Startup Has Sold So Far - The Daily Hodl