Global Automated Data Science and Machine Learning Platforms Market Size, Status and Forecast 2021

The Global Automated Data Science and Machine Learning Platforms Market Research Report 2021-2026 is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand, and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data that enhances the understanding, scope, and application of this report.

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Market Segmentation:

Key Players:Palantier, Microsoft, MathWorks, SAS, Databricks, Alteryx, H2O.ai, TIBCO Software, IBM, Dataiku, Domino, Altair, Google, RapidMiner, DataRobot, Anaconda, KNIME and others.

Segment by Types:Cloud-based

On-premises

Segment by Applications:Small and Medium Enterprises (SMEs)

Large Enterprises

Regions Are covered By Automated Data Science and Machine Learning Platforms Market Report 2021 To 2026

For comprehensive understanding of market dynamics, the global Automated Data Science and Machine Learning Platforms market is analyzed across key geographies namely: North America (United States, Canada, and Mexico), Europe (Germany, France, UK, Russia, and Italy), Asia-Pacific (China, Japan, Korea, India, and Southeast Asia), South America (Brazil, Argentina, and Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa). Each of these regions is analyzed on the basis of market findings across major countries in these regions for a macro-level understanding of the market.

Key Highlights of the Report

Quantitative market information and forecasts for the global Automated Data Science and Machine Learning Platforms industry, segmented by type, end-use, and geographic region.

Expert analysis of the key technological, demographic, economic, and regulatory factors driving growth in the Automated Data Science and Machine Learning Platforms to 2026.

Market opportunities and recommendations for new investments.

Growth prospects among the emerging nations through 2026.

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https://www.marketinsightsreports.com/reports/01122519203/global-automated-data-science-and-machine-learning-platforms-market-growth-status-and-outlook-2020-2025?Mode=P68

There are 13 Sections to show the global Automated Data Science and Machine Learning Platforms market:

Chapter 1:Market Overview, Drivers, Restraints and Opportunities, Segmentation overviewChapter 2:Market competition by ManufacturersChapter 3:Production by RegionsChapter 4:Consumption by RegionsChapter 5:Production, By Types, Revenue and Market share by TypesChapter 6:Consumption, By Applications, Market share (%) and Growth Rate by ApplicationsChapter 7:Complete profiling and analysis of ManufacturersChapter 8:Manufacturing cost analysis, Raw materials analysis, Region-wise manufacturing expensesChapter 9:Industrial Chain, Sourcing Strategy and Downstream BuyersChapter 10:Marketing Strategy Analysis, Distributors/TradersChapter 11:Market Effect Factors AnalysisChapter 12:Market ForecastChapter 13:Automated Data Science and Machine Learning Platforms Market Research Findings and Conclusion, Appendix, methodology and data source

Finally, researchers throw light on the pinpoint analysis of Global Automated Data Science and Machine Learning Platforms Market dynamics. It also measures the sustainable trends and platforms which are the basic roots behind the market growth. The degree of competition is also measured in the research report. With the help of SWOT and Porters five analysis, the market has been deeply analyzed. It also helps to address the risk and challenges in front of the businesses. Furthermore, it offers extensive research on sales approaches.

Note: All the reports that we list have been tracking the impact of COVID-19. Both upstream and downstream of the entire supply chain has been accounted for while doing this. Also, where possible, we will provide an additional COVID-19 update supplement/report to the report in Q3, please check for with the sales team.

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Automated Data Science and Machine Learning Platforms Market Technological Growth and Precise Outlook 2021- Microsoft, MathWorks, SAS, Databricks,...

As companies in the UAE scale up their adoption of Artificial Intelligence (AI) and Machine Learning (ML) implementation, a new report suggests that UAE organisations are now on par with their global counterparts in boasting mature capabilities in these fields.

Nonetheless, the vast majority of organisations in the wider EMEA regionincluding the UAEare still at the early stages of exploring the technologys potential (52%) or still require significant organisational work to implement an AI/ML solution (36%).

These are the key findings of new research from Rackspace Technology Inc, an end-to-end, multi-cloud technology solutions company, which revealed that the majority of organisations lack the internal resources to support critical AI and ML initiatives.The survey, Are Organisations Succeeding at AI and Machine Learning?,indicates that while many organisations are eager to incorporate AI and ML tactics into operations, they typically lack the expertise and existing infrastructure needed to implement mature and successful AI/ML programmes.

This study shines a light on the struggle to balance the potential benefits of AI and ML against the ongoing challenges of getting AI/ML initiatives off the ground. While some early adopters are already seeing the benefits of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI.

Other key findings of the report include the following:

Countries across EMEA, including the UAE, are lagging behind in AI and ML implementation, which can be hindering their competitive edge and innovation, said Simon Bennett, Chief Technology Officer, EMEA, Rackspace Technology. Globally were seeing IT decision-makers turn to these technologies to improve efficiency and customer satisfaction. Working with a trusted third-party provider, organisations can enhance their AI/ML projects moving beyond the R&D stage and into initiatives with long-term impacts.

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Rackspace Technology Study uncovers AI and Machine Learning knowledge gap in the UAE - Intelligent CIO ME

Longmont, CO, Feb. 09, 2021 (GLOBE NEWSWIRE) -- Parascript, which provides document analysis software processing for over 100 billion documents each year, announced today the Smart-Force (SFORCE) and Parascript partnership to provide a digital workforce that augments operations by combining cognitive Robotic Process Automation (RPA) technology with customers current investments for high scalability, improved accuracy and an enhanced customer experience in Mexico and across Latin America.

Partnering with Smart-Force means we get to help solve some of the greatest digital transformation challenges in Intelligent Document Processing instead of just the low-hanging fruit. Smart-Force is forward-thinking and committed to futureproofing their customers processes, even with hard-to-automate, unstructured documents where the application of techniques such as NLP is often required, said Greg Council, Vice President of Marketing and Product Management at Parascript. Smart-Force leverages bots to genuinely collaborate with staff so that the staff no longer have to spend all their time on finding information, and performing data entry and verification, even for the most complex multi-page documents that you see in lending and insurance.

Smart-Force specializes in digital transformation by identifying processes in need of automation and implementing RPA to improve those processes so that they run faster without errors. SFORCE routinely enables increased productivity, improves customer satisfaction, and improves staff morale through leveraging the technology of Automation Anywhere, Inc., a leader in RPA, and now Parascript Intelligent Document Processing.

As intelligent automation technology becomes more ubiquitous, it has created opportunities for organizations to ignite their staff towards new ways of working freeing up time from the manual tasks to focus on creative, strategic projects, what humans are meant to do, said Griffin Pickard, Director of Technology Alliance Program at Automation Anywhere. By creating an alliance with Parascript and Smart-Force, we have enabled customers to advance their automation strategy by leveraging ML and accelerate end-to-end business processes.

Our focus at SFORCE is on RPA with Machine Learning to transform how customers are doing things. We dont replace; we compliment the technology investments of our customers to improve how they are working, said Alejandro Castrejn, Founder of SFORCE. We make processes faster, more efficient and augment their staff capabilities. In terms of RPA processes that focus on complex document-based information, we havent seen anything approach what Parascript can do.

We found that Parascript does a lot more than other IDP providers. Our customers need a point-to-point RPA solution. Where Parascript software becomes essential is in extracting and verifying data from complex documents such as legal contracts. Manual data entry and review produces a lot of errors and takes time, said Barbara Mair, Partner at SFORCE. Using Parascript software, we can significantly accelerate contract execution, customer onboarding and many other processes without introducing errors.

The ability to process simple to very complex documents such as unstructured contracts and policies within RPA leveraging FormXtra.AI represents real opportunities for digital transformation across the enterprise. FormXtra.AI and its Smart Learning allow for easy configuration, and by training the systems on client-specific data, the automation is rapidly deployed with the ability to adapt to new information introduced in dynamic production environments.

About SFORCE, S.A. de C.V.

SFORCE offers services that allow customers to adopt digital transformation at whatever pace the organization needs. SFORCE is dedicated to helping customers get the most out of their existing investments in technology. SFORCE provides point-to-point solutions that combine existing technologies with next generation technology, which allows customers to transform operations, dramatically increase efficiency as well as automate manual tasks that are rote and error-prone, so that staff can focus on high-value activities that significantly increase revenue. From exploring process automation to planning a disruptive change that ensures high levels of automation, our team of specialists helps design and implement the automation of processes for digital transformation. Visit SFORCE.

About Parascript

Parascript software, driven by data science and powered by machine learning, configures and optimizes itself to automate simple and complex document-oriented tasks such as document classification, document separation and data entry for payments, lending and AP/AR processes. Every year, over 100 billion documents involved in banking, insurance, and government are processed by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Visit Parascript.

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Parascript and SFORCE Partner to Leverage Machine Learning Eliminating Barriers to Automation - GlobeNewswire

Delivering fast and reliable machine learningbusinesssolutions

JERSEY CITY, N.J., Feb. 9, 2021 /PRNewswire/ --ElectrifAi, one of the world's leading companies inpractical artificial intelligence (AI) and pre-built machine learning models, today announcedit has a new and improved spend analytics and procurement tool called SpendAi.

What makes SpendAi different from other products on the market? SpendAi combines the power of machine learning models to construct a solid foundation of a high-quality comprehensive data set and a highly configurable user experience. ElectrifAi puts its industry leading data cleansing and structuring expertise to practical use in this solution. Our scientists and engineers have applied their unique skill sets to produce the most highly automated and effective data transformation architecture in the market. This bedrock of data then enables a uniquely configurable experience to the end user. The industry has been lacking a flexible tool such as SpendAi. Every company has a different way of looking at procurement and categorizing their vendors and spend. SpendAi is the only tool on the market that gives companies the ability to change the vendor and spend classification on their own.

Why is machine learning important? How does machine learning change spend analytics? ElectrifAi's machine learning drastically reduces unclassified and misclassified spend, giving procurement professionals a much clearer picture of their vendor leverage and dependencies. It also provides far greater insight to maverick and off-contract spend, optimization of discount opportunities along with other features. In short, users have much more visibility into their risks and opportunities. AI is then used to find and prioritize those risks and opportunities. As a result, teams spend less time searching and more time acting on insights. This turns procurement into a strategic business partner for the business.

SpendAi enables companies to look deeply into their data and generates insights that procurement professionals can use right away. Making structural changes on their own is also very simple with this tool and they don't have to pay a professional or wait overnight for results. This again gives people a way to look at procurement strategically, not just reactively or pulled together haphazardly.

Companies can now quickly analyze all their dataincluding direct and indirect spend materials and servicesacross every system they use to get insights into how they can reduce costs and improve their cash position, all in one convenient location. The flexibility of SpendAi is very user friendly and enables users to make quick and comprehensive decisions.

Insights provided by the machine learning capabilities of SpendAi allow companies to spot unexpected or disadvantageous spend patterns that warrant further attention. SpendAi gives them a prioritized list of things to look at and consider as either risk or savings opportunities or something that looks amiss.

Nisreen Bagasra, Chief Procurement Officer from Veolia said: "We're looking forward to SpendAi because of the flexibility it provides. This tool is going to allow us to be more dynamic and accelerate our business. This is like nothing we've seen in the market before. There's never been a way to see all your data in one place before. This is the first tool that uses machine learning to organize the system and tie all the data with high-quality so you can really be strategic. This is a new generation of spend analytics."

About ElectrifAiElectrifAi is a global leader in business-ready machine-learning models. ElectrifAi's missionis tohelp organizations change the way they work through machine learning: driving costreductionas wellasprofit and performance improvement. Founded in 2004,ElectrifAi boastsseasoned industryleadership,aglobal team of domain experts, andaproven record oftransforming structured and unstructured data at scale.A large library ofAI-basedproductsreachesacrossbusiness functions, data systems, and teams to drive superior resultsin record time. ElectrifAi has approximately 200 data scientists, software engineers andemployees with a proven record of dealing with over 2,000 customer implementations,mostly for Fortune 500 companies. At the heart of ElectrifAi's mission is a commitment tomakingAI and machine learning more understandable, practical and profitable forbusinesses andindustries across the globe. ElectrifAi is headquartered in Jersey City, withoffices located in Shanghai and New Delhi.

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ElectrifAi Announces Updates to SpendAi, an Innovative and Flexible Procurement Tool - PRNewswire

Advanced computer programs that use machine learning are transforming the way medical research is done in Nova Scotia and around the world.

Work that might have taken years to complete, or would have been astronomically expensive, can now be done faster and at lower cost.

It has allowed teams in this province to develop better ways to identify and treat cancer, discover new drugs to help blind children see, and speed up medical tests.

Acomputer program learns from data and identifies patterns with little human intervention. A more advanced form of machine learning is often referred to as neural networks.

For example, a program can be shown millions of pictures of cars and, eventually, it will identify a particular car, says Thomas Trappenberg, a Dalhousie University computer science professor.

Medical researchers have turned that learning power inward, setting up programs to recognize cancer cells and proteins.

One group is trying to figure out how to better identify the differences between cancer cells and healthy cells,and, in doing so, what drug treatments will work best for an individual.

"Target discovery is very important right now," said Brendan Leung, an assistant professor in applied oral sciences at Dalhousie. "So knowing what to hit is just as important as designing the weapon to hit it."

The research team he's part of also includes a tumour biologist and computer scientist.

Leung hopes the technology will eventually allow scientists to design drugs to better target cancer cells without harming healthy cells.

He said the research would be almost impossible without a computer capable of machine learning.

"With all this big data it just surpasses humans' ability to comprehend what is going on," he said.

"Not to mention human beings are notoriously biased.If you've been working with a particular gene for the past 20 years, you know, it's your favourite thing to look at, you will find what you want to see. So the way I see it, it's a great way to take away that bias."

Leung said software can be biased as well, but perhaps not as biased as a person.

Machine learning has already helped develop new drugs that treat a rare hereditary disease that can cause children to go blind.

The disease is called Familial Exudative Vitreoretinopathy, orFEVR.It prevents the proper amount of blood from reaching the eye.

Depending on severity, it can result in poor vision or blindness, said Christopher McMaster, a Dalhousie professor of pharmacology.

McMaster's goal is to turn off a protein that prevents the arteries and veins in the eye from growing properly. The computer uses all available information to create a three-dimensional model of what that protein could look like.

"Once you have this three-dimensional picture you can then use the AI to say, 'OK,I need to stick a drug-like molecule essentially into the gears of this protein to turn it off. Give me a list of drugs, not known drugs but anything you could synthesize in a lab that we could stick into this spot that could turn it on or off,'" said McMaster.

The system has worked.McMaster and his team have created a drug that treats FEVR.

"If you were a mouse with FEVR right now we could restore your vision quite well," he said.

It will be a year or longer before McMaster files the documents to start human trials.

Doing this work without computers capable of machine learning would have been challenging as thousands, evenmillions, of drugs would need to be tested in a lab,as opposed to the computer running virtual tests, said McMaster.

"Diseases like this one that don't affect a lot of children, they'd not have any shot at a therapy whatsoever," he said. "So this has really opened up the avenue for a lot of different diseases that would never see the light of day."

That's not the only success story in the province.

Another professor at Dalhousie has helped develop a device that can quickly perform a blood test without a technician or doctor present.

Alan Fine is a professor with the faculty of medicine in the school of biomedical engineering. He's also founder of the company Alentic Microscience.

Fine developed a device, called the Prospector, that isabout the size of a debit machine and can take images of blood cells with a sensor. The machine's neural network has been taught to recognize different parts of the blood and perform a complete blood count.

That test can tell the number of red blood cells, the number of white cells, platelets and gather other information.

"It's a sort of snapshot image of the overall health of an individual and it provides clues to many different kinds of illnesses," said Fine.

In the best-case scenario, the traditional test for a complete blood count would take 20 minutes.More often it can take hours or a day to get results back, said Fine.

His device takes five minutes and is portable.

Right now, the machine is in its testing form and hasn't yet been approved for diagnostic use by Health Canada or other regulatory agencies.Fine hopes those approvals will come later this year.

"These neural network approaches, they have proven so massively effective," said Fine.

"We were very early beneficiaries of this novel computing technology. It's totally transformed the way that we do this and as I think you can see it's not just our little application, it's spreading throughout medicine."

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How machine learning is revolutionizing medical research in Nova Scotia and beyond - CBC.ca

AI and machine learning are revolutionizing modern businesses - here's how to get ahead

By Pierre Fricke- February 4, 2021

Fierce competition means every business must adapt to succeed. AI and machine learning have emerged as modern, vital ways for organizations to get ahead. Many businesses today prioritize data, analytics and AI/machine learning projects to power new business models, enhance product and service offerings, improve efficiency, drive revenue and deliver superior customer experiences.

But analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and machine learning projects will be successfully deployed in production by 2022. Less than a fifth will move piloted AI projects into production without delays caused by a range of problems - from technical skills gaps and lack of IT/business process maturity, to insufficient organizational collaboration.

For example, these businesses may not have expertise in mathematics, algorithm design or data science and engineering. Or their data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any organization looking to advance in the market and derive value from AI and machine learning.

This combination of pressure and challenges can overwhelm your business, especially if you're at the start of your AI and machine learning journey. So let's dig into why your business should make the effort - and how doing so might require different skills sets and data from what you might think.

Let's start with the basics. When a machine completes tasks based on a set of stipulated rules that solve problems, we're into the realm of artificial intelligence. This might include understanding and interpreting natural language, recognizing when objects move and providing intelligent answers. Business benefits follow, such as analyzing data sets that are too large for humans to process, answering questions in real time that draw from existing data and experiences, and automation that can reduce costs and boost productivity.

Machine learning is a discipline within the AI domain. It enables machines to learn by themselves using data. They use this knowledge to make increasingly accurate predictions and drive actions. For this to happen, you need a model that's trained on existing data, after which point it can process additional data and make predictions. Throughout the process, it's important to track and understand your model, building quality and eliminating bias.

Finally, deep learning is a subfield of machine learning. It structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own.

We've so far explored AI, machine learning and deep learning in the abstract, but in what specific ways can they benefit your business?

If you're looking to machine learning and deep learning but have concerns about your existing data, be mindful that they don't always need massive data sets. While completely new models with no data nor training do require tens of thousands to millions of data points, trained models exist that can give a project leader a head start. Even if you have just 100 or so examples for a specific use case, building on a general model's foundation could yield more accurate results than human experts would provide.

Additionally, it's worth thinking differently about hiring for the delivery of AI/machine learning enabled applications and solutions. There's an assumption you need PhD-level data scientists. Although they do add value and can be necessary in some circumstances, existing staff can often be trained in about 100 hours, building on high-school math and a year of coding experience. With modern tools on AWS or Google Cloud including AutoML, they can build the solutions you need.

In all, it's as much about changing your mindset as anything else. You must think about what AI and machine learning can bring to your business and the most effective way to achieve that, thereby keeping your company ahead. Machine learning is today driving change in thinking of data as code - where machine learning uses data to write the program, which is the output.

This methodology coupled with the tools and education I mentioned earlier set the stage for many more people collaborating to fashion a new generation of intelligent solutions that will revolutionize business for years to come.

For more information on AI and machine learning, check out our panel discussion, which dives deep into these topics. The discussion covers: toolsets and methodologies; capabilities and constraints; data, computer and expertise requirements; examples of successful applications; and how to get started.

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Rackspace Technology Inc. published this content on 04 February 2021 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 08 February 2021 22:08:06 UTC.

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Rackspace Technology : AI and machine learning are revolutionizing modern businesses here's how to get ahead - Marketscreener.com

To improve speed and intelligence of threat detection and response, Guruculs cloud-native XDR platform is adding machine learning, integration risk scoring and more.

by Anne Lessman

Tags: cloud-native, Gurucul, integration, machine learning, real-time, threat detection,

The latest upgrade to the Gurucul XDR platform adds extended detection and response alongside improved risk scoring to strengthen security operations effectiveness and productivity.

Improvements to Guruculs cloud-native solution also sport features to enable intelligent investigations and risk-based response automation. New features include extended data linking, additions to its out-of-the-box integrations, contextual machine learning (ML) analytics and risk-prioritized alerting.

The driving force behind these updates is to provide users a single pane of risk, according to Gurucul CEO Saryu Nayyar.

Most XDR products are based on legacy platforms limited to siloed telemetry and threat detection, which makes it difficult to provide unified security operations capabilities, Nayyar said.

Gurucul Cloud-native XDR is vendor-agnostic and natively built on a Big Data architecture designed to process, contextually link, analyze, detect, and risk score using data at massive scale. It also uses contextual Machine Learning models alongside a risk scoring engine to provide real-time threat detection, prioritize risk-based alerts and support automated response, Nayyar.added.

Gurucul XDR provides the following capabilities that are proven to improve incident response times:

AI/ML Suggestive Investigation and Automated Intelligent Responses: Traditional threat hunting tools and SIEMs focus on a limited number of use cases since they rely on data and alerts from a narrow set of resources. With cloud adoption increasing at a record pace, threat hunting must span hybrid on-premises and cloud environments and ingest data from vulnerability management, IoT, medical, firewall, network devices and more.

Guruculs approach provides agentless, out-of-the-box integrations that support a comprehensive set of threat hunting applications. These include: Insider threat detection, Data exfiltration, Phishing, Endpoint forensics, Malicious processes and Network threat analytics.

Incident Timeline, Visualizations, and Reporting: Automated Incident Timelines create a smart link of the entire attack lifecycle for pre-and post-incident analysis. Timelines can span days and even years of data in easy-to-understand visualizations.

Guruculs visualization and dashboarding enables analysts to view threats from different perspectives using several widgets, including TreeMap, Bubble Chart, etc., that provide full drill-down capabilities into events without leaving the interface. The unique scorecard widget generates a spider chart representation of cyber threat hunting outcomes such as impact, sustaining mitigation measures, process improvements scores, etc.

Risk Prioritized Automated Response: Integration with Gurucul SOAR enables analysts to invoke more than 50 actions and 100 playbooks upon detection of a threat to minimize damages.

Entity Based Threat Hunting: Perform contextual threat hunting or forensics on entities. Automate and contain any malicious or potential threat from a single interface.

Red Team Data Tagging: Teams can leverage red team exercise data and include supervised learning techniques as part of a continuous AI-based threat hunting process.

According to Gartner, XDR products aim to solve the primary challenges with SIEM products, such as effective detection of and response to targeted attacks, including native support for behavior analysis, threat intelligence, behavior profiling and analytics.

Further, the primary value propositions of an XDR product are to improve security operations productivity and enhance detection and response capabilities by including more security components into a unified whole that offers multiple streams of telemetry, Gartner added.

The result, the firm said, is to present options for multiple forms of detection and . . multiple methods of response.

Gurucul XDR provides the following capabilities that are proven to improve incident response times by nearly 70%:

Surgical Response

Intelligent Centralized Investigation

Rapid Incident Correlation and Causation

Gurucul XDR is available immediately from Gurucul and its business partners worldwide.

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Gurucul XDR Uses Machine Learning & Integration for Real-Time Threat Detection, Incident Response - Integration Developers

The classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning

Technology in the classroom is becoming more and more popular as we pass through the 21st century. Laptops are replacing our textbooks, and on our smart phones, we can study just about everything we want. Social media has become ubiquitous, and the way we use technology has changed the way we live our lives fully.

Technology has become the core component of distance education programs. It enhances teachers and students to digitally interconnect and exchange material and student work, retaining a human link, which is important for the growth of young minds. Enhanced connections and customized experience can allow educators torecognizeopportunities for learning skills and enhance the potential of a student.

Hence, the classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning.

Machine learning is an artificial intelligence (AI) element, which lets machines or computers learn from all previous knowledge and make smart decisions. The architecture for machine learning involves gathering and storing a rich collection of information and turning it into a standardized knowledge base for various uses in different fields. Educators could save time in their non-classroom practices in the field of education by concentrating on machine learning.

For instance, teachers may use virtual helpers to work for their students directly from home. This form of assistance helps to boost the learning environment of students and can promote growth and educational success.

According to ODSC, Last years report by MarketWatch has revealed that Machine Learning in education will remain one of the top industries to drive investment, with the U.S. and China becoming the top key players by 2030. Major companies, like Google and IBM, are getting involved in making school education more progressive and innovative.

Analyzing all-round material

By making the content more up-to-date and applicable to an exact request, the use of machine learning in education aims to bring the online learning sector to a new stage. How? ML technologies evaluate the content of courses online and help to assess whether the quality of the knowledge presented meets the applicable criteria. On the other hand, know how users interpret the data and understand what is being explained. Users then obtain the data according to their particular preferences and expertise, and the overall learning experience increases dramatically.

Customized Learning

This is the greatest application of machine learning. It is adaptable and it takes care of individual needs. Students are able to guide their own learning through this education system. They can have theirown speed and decide what to study and how to learn. They can select the topics they are interested in, the instructor they want to learn from, and what program they want to pursue, expectations and trends.

Effective Grading

In education, there is another application of machine learning that deals with grades and scoring. Since the learning skills of a large number of students are expressed in each online course, grading them becomes a challenge. ML technology makes the grading process a few seconds problem. In this context, we talk more about the exact sciences. There are places where teachers cannot be replaced by computers, but even in such situations, they can contribute to enhance current approaches of grading and evaluation.

According to TechXplore, Researchers at University of Tbingen and Leibniz Institute fr Wissensmedien in Germany, as well as University of Colorado Boulder, have recently investigated the potential of machine-learning techniques for assessing student engagement in the context of classroom research. More specifically, they devised a deep-neural-network-based architecture that can estimate student engagement by analyzing video footage collected in classroom environments.

They also mentioned that, We used camera data collected during lessons to teach a deep-neural-network-based model to predict student engagement levels, Enkelejda Kasneci the leading HCI researcher in the multidisciplinary team that carried out the study, told TechXplore. We trained our model on ground-truth data (e.g., expert ratings of students level of engagement based on the videos recorded in the classroom). After this training, the model was able to predict, for instance, whether data obtained from a particular student at a particular point in time indicates high or low levels of engagement.

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Can Machine Learning be the Best Remedy in the Education Sector? - Analytics Insight

REACH is available in award-winning Millennium System International's scheduling software product, Meevo 2, and serves thousands of beauty businesses in over 30 countries."We are thrilled to announce another Meevo 2 business building integration offering within our MeevoXchange marketplace REACH by Octopi. REACH delivers the AI-powered smart scheduling features to help keep our salons and spas booked and growing. This partnership aligns with our strategic goals for our award-winning software Meevo 2 as we continuously add value to our platform and ultimately our salon and spa customers," says CEO John Harms, Millennium Systems International.

"REACH is so special because it requires virtually no setup or upkeep as it follows your existing Meevo 2 online booking settings. REACH plays 'matchmaker' by connecting your clients that are due and overdue with open spaces in your Meevo 2 appointment book over the next few days, automatically. It has taken us years of research and development to create such successful and exciting tool that will begin to show value to your business starting on day one!" CEO Patrick Blickman, REACH by Octopi

Performance Guarantee and Affordability

The platform includes the REACH Revenue Guarantee thatensures each location will see a minimum of $600-$1400 in new booking revenue every month. There are never any contracts or commitments with REACH. Simply turn it on and let it start filling your Meevo 2 appointment book. Pricing starts at $149/month.

About REACH by OCTOPI

REACH was founded to make the client booking experience easier and far more automated for the health and beauty businesses we serve. Headquartered in Scottsdale, Arizona; REACH is built on decades of consolidated industry and channel expertise. Visitwww.octopi.com/reach

About Millennium Systems International:

Millennium Systems International has been a leading business management software for the salon, spa and wellness industry for more than three decades. The award-winning Meevo 2 platform provides a true cloud-based business management software that is HIPAA compliant and fully responsive, so users can gain complete access using any device, built by wellness and beauty veterans exclusively for the wellness and beauty industry. Visit https://www.millenniumsi.com

SOURCE Octopi

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REACH and Millennium Systems International Partner to offer Machine Learning Driven Booking Automation to the MeevoXchange Marketplace - PRNewswire

05 February 2021

Researchers in EPFLs Digital and Cognitive Musicology Lab in the College of Humanities used an unsupervised machine learning model to reveal how modes such as major and minor have changed throughout history.

Many people may not be able to define what a minor mode is in music, but most would almost certainly recognise a piece played in a minor key. Thats because we intuitively differentiate the set of notes belonging to the minor scale which tend to sound dark, tense, or sad from those in the major scale, which more often connote happiness, strength, or lightness.

But throughout history, there have been periods when multiple other modes were used in addition to major and minor or when no clear separation between modes could be found at all.

Understanding and visualising these differences over time is what Digital and Cognitive Musicology Lab (DCML) researchers Daniel Harasim, Fabian Moss, Matthias Ramirez, and Martin Rohrmeier set out to do in a recent study, which has been published in the open-access journal Humanities and Social Sciences Communications. For their research, they developed a machine learning model to analyze more than 13,000 pieces of music from the 15th to the 19th centuries, spanning the Renaissance, Baroque, Classical, early Romantic, and late-Romantic musical periods.

We already knew that in the Renaissance [1400-1600], for example, there were more than two modes. But for periods following the Classical era [1750-1820], the distinction between the modes blurs together. We wanted to see if we could nail down these differences more concretely, Harasim explains.

Machine listening (and learning)

The researchers used mathematical modelling to infer both the number and characteristics of modes in these five historical periods in Western classical music. Their work yielded novel data visualizations showing how musicians during the Renaissance period, like Giovanni Pierluigi da Palestrina, tended to use four modes, while the music of Baroque composers, like Johann Sebastian Bach, revolved around the major and minor modes. Interestingly, the researchers could identify no clear separation into modes of the complex music written by Late Romantic composers, like Franz Liszt.

Harasim explains that the DCMLs approach is unique because it is the first time that unlabelled data have been used to analyse modes. This means that the pieces of music in their dataset had not been previously categorized into modes by a human.

We wanted to know what it would look like if we gave the computer the chance to analyse the data without introducing human bias. So, we applied unsupervised machine learning methods, in which the computer 'listens' to the music and figures out these modes on its own, without metadata labels.

Although much more complex to execute, this unsupervised approach yielded especially interesting results which are, according to Harasim, more cognitively plausible with respect to how humans hear and interpret music.

We know that musical structure can be very complex and that musicians need years of training. But at the same time, humans learn about these structures unconsciously, just as a child learns a native language. Thats why we developed a simple model that reverse engineers this learning process, using a class of so-called Bayesian models that are used by cognitive scientists, so that we can also draw on their research.

From class project to publicationand beyond

Harasim notes with satisfaction that this study has its roots in a class project that he and his co-authors Moss and Ramirez did together as students in EPFL professor Robert Wests course, Applied Data Analysis. He hopes to take the project even further by applying their approach to other musical questions and genres.

For pieces within which modes change, it would be interesting to identify exactly at what point such changes occur. I would also like to apply the same methodology to jazz, which was the focus of my PhD dissertation because the tonality in jazz is much richer than just two modes.

Read more from the original source:
- Retracing the evolution of classical music with machine learning - Design Products & Applications