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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Thanks for reading this article, you can also get individual chapter wise section or region wise report versions like North America, Western / Eastern Europe or Southeast Asia.

With the given market data, Research on Global Markets offers customizations according to specific needs. Write to AMR at [emailprotected], or connect via +1-530-868-6979

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

Silicon Integration Initiativehas launched an industry-wide survey to identify planned usage and structural gaps for prioritizing and implementing artificial intelligence and machine learning in semiconductor electronic design automation.

The Si2 platform provides a unique opportunity for semiconductor companies, EDA suppliers and IP providers to voice their needs and focus resources on common solutions, including enabling and leveraging university research.

The survey is organized by a recently formed Si2 Special Interest Group chaired by Joydip Das, senior engineer, Samsung Electronics, and co-chaired by Kerim Kalafala, senior technical staff member, EDA, and master inventor, IBM. The 18-member group will identify where industry collaboration will help eliminate deficiencies caused by a lack of common languages, data models, labels, and access to robust and categorized training data.

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This SIG is open to all Si2 members. Current members include:

Advanced Micro DevicesANSYSCadence Design SystemsHewlett Packard EnterpriseIBMIntelIntento DesignKeysight TechnologiesMentor, a Siemens Business

NC State UniversityPFD Solutions

Qualcomm

Samsung Electronics

Sandia National LaboratoriesSilvaco

SynopsysThrace SystemsTexas Instruments

The survey is open April 15 May 15.

Leigh Anne Clevenger, Si2 senior data scientist, said that the survey results would help prioritize SIG activities and timelines. The SIG will identify and develop requirements for standards that ensure data and software interoperability, enabling the most efficient design flows for production, Clevenger said. The ultimate goal is to remove duplicative work and the need for data model translators, and focus on opening avenues for breakthroughs from suppliers and users alike.

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High manufacturing costs and the growing complexity of chip development are spurring disruptive technologies such as AI and ML, Clevenger explained. The Si2 platform provides a unique opportunity for semiconductor companies, EDA suppliers and IP providers to voice their needs and focus resources on common solutions, including enabling and leveraging university research.

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si2 Launches Survey on Artificial Intelligence and Machine Learning in Eda - AiThority

Machine learning has fundamentally changed how we engage with technology. Today, it's able to curate social media feeds, recognize complex images, drive cars down the interstate, and even diagnose medical conditions, to name a few tasks.

But while machine learning technology can do some things automatically, it still requires a lot of input from human engineers to set it up, and point it in the right direction. Inevitably, that means human biases and limitations are baked into the technology.

So, what if scientists could minimize their influence on the process by creating a system that generates its own machine-learning algorithms? Could it discover new solutions that humans never considered?

To answer these questions, a team of computer scientists at Google developed a project called AutoML-Zero, which is described in a preprint paper published on arXiv.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," the paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

Automatic machine learning (AutoML) is a fast-growing area of deep learning. In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems. Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.

AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms "from scratch," as the paper states. Then, it selects the best ones, and mutates them through a process that's similar to Darwinian evolution.

AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image. The performance of these algorithms is compared to hand-designed algorithms. AutoML-Zero then selects the top-performing algorithm to be the "parent."

"This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed," the paper states.

The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

If computer scientists can scale up this kind of automated machine-learning to complete more complex tasks, it could usher in a new era of machine learning where systems are designed by machines instead of humans. This would likely make it much cheaper to reap the benefits of deep learning, while also leading to novel solutions to real-world problems.

Still, the recent paper was a small-scale proof of concept, and the researchers note that much more research is needed.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

Related Articles Around the Web

Continued here:
New AI improves itself through Darwinian-style evolution - Big Think

The COVID-19 pandemic is raising critical questions regarding the dynamics of the disease, its risk factors, and the best approach to address it in healthcare systems. MIT Sloan School of Management Prof. Dimitris Bertsimas and nearly two dozen doctoral students are using machine learning and optimization to find answers. Their effort is summarized in the COVIDanalytics platform where their models are generating accurate real-time insight into the pandemic. The group is focusing on four main directions; predicting disease progression, optimizing resource allocation, uncovering clinically important insights, and assisting in the development of COVID-19 testing.

The backbone for each of these analytics projects is data, which weve extracted from public registries, clinical Electronic Health Records, as well as over 120 research papers that we compiled in a new database. Were testing our models against incoming data to determine if it makes good predictions, and we continue to add new data and use machine-learning to make the models more accurate, says Bertsimas.

The first project addresses dilemmas at the front line, such as the need for more supplies and equipment. Protective gear must go to healthcare workers and ventilators to critically ill patients. The researchers developed an epidemiological model to track the progression of COVID-19 in a community, so hospitals can predict surges and determine how to allocate resources.

The team quickly realized that the dynamics of the pandemic differ from one state to another, creating opportunities to mitigate shortages by pooling some of the ventilator supply across states. Thus, they employed optimization to see how ventilators could be shared among the states and created an interactive application that can help both the federal and state governments.

Different regions will hit their peak number of cases at different times, meaning their need for supplies will fluctuate over the course of weeks. This model could be helpful in shaping future public policy, notes Bertsimas.

Recently, the researchers connected with long-time collaborators at Hartford HealthCare to deploy the model, helping the network of seven campuses to assess their needs. Coupling county level data with the patient records, they are rethinking the way resources are allocated across the different clinics to minimize potential shortages.

The third project focuses on building a mortality and disease progression calculator to predict whether someone has the virus, and whether they need hospitalization or even more intensive care. He points out that current advice for patients is at best based on age, and perhaps some symptoms. As data about individual patients is limited, their model uses machine learning based on symptoms, demographics, comorbidities, lab test results as well as a simulation model to generate patient data. Data from new studies is continually added to the model as it becomes available.

We started with data published in Wuhan, Italy, and the U.S., including infection and death rate as well as data coming from patients in the ICU and the effects of social isolation. We enriched them with clinical records from a major hospital in Lombardy which was severely impacted by the spread of the virus. Through that process, we created a new model that is quite accurate. Its power comes from its ability to learn from the data, says Bertsimas.

By probing the severity of the disease in a patient, it can actually guide clinicians in congested areas in a much better way, says Bertsimas.

Their fourth project involves creating a convenient test for COVID-19. Using data from about 100 samples from Morocco, the group is using machine-learning to augment a test previously designed at the Mohammed VI Polytechnic University to come up with more precise results. The model can accurately detect the virus in patients around 90% of the time, while false positives are low.

The team is currently working on expanding the epidemiological model to a global scale, creating more accurate and informed clinical risk calculators, and identifying potential ways that would allow us to go back to normality.

We have released all our source code and made the public database available for other people too. We will continue to do our own analysis, but if other people have better ideas, we welcome them, says Bertsimas.

Original post:
Research Team Uses Machine Learning to Track Covid-19 Spread in Communities and Predict Patient Outcomes - The Ritz Herald

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

Futuristic Reports, The growth and development of Global Machine Learning as a Service (MLaaS) Market Report 2020 by Players, Regions, Type, and Application, forecast to 2026 provides industry analysis and forecast from 2020-2026. Global Machine Learning as a Service (MLaaS) Market analysis delivers important insights and provides a competitive and useful advantage to the pursuers. Machine Learning as a Service (MLaaS) processes, economic growth is analyzed as well. The data chart is also backed up by using statistical tools.

Simultaneously, we classify different Machine Learning as a Service (MLaaS) markets based on their definitions. Downstream consumers and upstream materials scrutiny are also carried out. Each segment includes an in-depth explanation of the factors that are useful to drive and restrain it.

Key Players Mentioned in the study are Broadcom, EMC, GEMALTO, SYMANTEC, VASCO DATA SECURITY INTERNATIONAL, AUTHENTIFY, ENTRUST DATACARD, SECUREAUTH, SECURENVOY, TELESIGN

For Better Understanding, Download FREE Sample Copy of Machine Learning as a Service (MLaaS) Market Report @ https://www.futuristicreports.com/request-sample/67627

Key Issues Addressed by Machine Learning as a Service (MLaaS) Market: It is very significant to have Machine Learning as a Service (MLaaS) segmentation analysis to figure out the essential factors of growth and development of the market in a particular sector. The Machine Learning as a Service (MLaaS) report offers well summarized and reliable information about every segment of growth, development, production, demand, types, application of the specific product which will be useful for players to focus and highlight on.

Businesses Segmentation of Machine Learning as a Service (MLaaS) Market:

On the basis on the applications, this report focuses on the status and Machine Learning as a Service (MLaaS) outlook for major applications/end users, sales volume, and growth rate for each application, including-

BFSI MarketMedical MarketThe IT MarketThe Retail MarketEntertainment MarketLogistics MarketOther

On the basis of types/products, this Machine Learning as a Service (MLaaS) report displays the revenue (Million USD), product price, market share, and growth rate of each type, split into-

Small And Medium-Sized EnterprisesBig Companies

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NOTE : Our team is studying Covid-19 impact analysis on various industry verticals and Country Level impact for a better analysis of markets and industries. The 2020 latest edition of this report is entitled to provide additional commentary on latest scenario, economic slowdown and COVID-19 impact on overall industry. Further it will also provide qualitative information about when industry could come back on track and what possible measures industry players are taking to deal with current situation.

or

You just drop an Email to: [emailprotected] us if you are looking for any Economical analysis to shift towards the New Normal on any Country or Industry Verticals.

Machine Learning as a Service (MLaaS) Market Regional Analysis Includes:

Asia-Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia) Europe (Turkey, Germany, Russia UK, Italy, France, etc.) North America (the United States, Mexico, and Canada.) South America (Brazil etc.) The Middle East and Africa (GCC Countries and Egypt.)

Machine Learning as a Service (MLaaS) Insights that Study is going to provide:

Gain perceptive study of this current Machine Learning as a Service (MLaaS) sector and also possess a comprehension of the industry; Describe the Machine Learning as a Service (MLaaS) advancements, key issues, and methods to moderate the advancement threats; Competitors In this chapter, leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue. A separate chapter on Machine Learning as a Service (MLaaS) market structure to gain insights on Leaders confrontational towards market [Merger and Acquisition / Recent Investment and Key Developments] Patent Analysis** Number of patents filed in recent years.

Table of Content:

Global Machine Learning as a Service (MLaaS) Market Size, Status and Forecast 20261. Market Introduction and Market Overview2. Industry Chain Analysis3. Machine Learning as a Service (MLaaS) Market, by Type4. Machine Learning as a Service (MLaaS) Market, by Application5. Production, Value ($) by Regions6. Production, Consumption, Export, Import by Regions (2016-2020)7. Market Status and SWOT Analysis by Regions (Sales Point)8. Competitive Landscape9. Analysis and Forecast by Type and Application10. Channel Analysis11. New Project Feasibility Analysis12. Market Forecast 2020-202613. Conclusion

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Machine Learning as a Service (MLaaS) Market Significant Growth with Increasing Production to 2026 | Broadcom, EMC, GEMALTO - Cole of Duty

The Respond Analyst Simplifies Endpoint Analysis, Delivers Real-Time, Expert Diagnosis of Security Incidents at a Fraction of the Cost of Manual Monitoring and Investigation

Respond Software today announced analysis support of Endpoint Detection and Response (EDR) data from Carbon Black, CrowdStrike and SentinelOneby the Respond Analyst the virtual cybersecurity analyst for security operations. The Respond Analyst provides customers with expert EDR analysis right out of the box, creating immediate business value in security operations for organizations across industries.

The Respond Analyst provides a highly cost-effective and thorough way to analyze security-related alerts and data to free up people and budget from initial monitoring and investigative tasks. The software uses integrated reasoning decision-making that leverages multiple alerting telemetries, contextual sources and threat intelligence to actively monitor and triage security events in near real-time. Respond Software is now applying this unique approach to EDR data to reduce the number of false positives from noisy EDR feeds and turn transactional sensor data into actionable security insights.

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Mike Armistead, CEO and co-founder, Respond Software, said: As security teams increase investment in EDR capabilities, they not only must find and retain endpoint analysis capabilities but also sift through massive amounts of data to separate false positives from real security incidents. The Respond Analyst augments security personnel with our unique Robotic Decision Automation software that delivers thorough, consistent and 24x7x365 analysis of security data from network to endpoint saving budget and time for the security team. It derivesmaximum value from EDR at a level of speed and efficiency unmatched by any other solution today.

Jim Routh,head of enterprise information risk management,MassMutual, said:Data science is the foundation for MassMutuals cybersecurity program.Applying mathematics and machine learning models to security operations functions to improve productivity and analytic capability is an important part of this foundation.

Jon Davis, CEO of SecureNation, said:SecureNation has made a commitment to its customers to deliver the right technology that enables the right security automation at lower operating costs. The EDR skills enabled by the Respond Analyst will make it possible for SecureNation to continue to provide the most comprehensive, responsive managed detection and response service available to support the escalating needs of enterprises today and into the future.

Recommended AI News: Tech Taking Over Our Lives: Smart Phones and the Internet of Things (IoT)

EDR solutions capture and evaluate a broad spectrum of attacks spanning the MITRE ATT&CK Framework. These products often produce alerts with a high degree of uncertainty, requiring costly triage by skilled security analysts that can take five to 15 minutes on average to complete. A security analyst must pivot to piece together information from various security product consoles, generating multiple manual queries per system, process and account. The analyst must also conduct context and scoping queries. All this analysis requires deep expert system knowledge in order to isolate specific threats.

The Respond Analyst removes the need for multiple console interactions by automating the investigation, scoping and prioritization of alerts into real, actionable incidents. With the addition of EDR analysis, Respond Software broadens the integrated reasoning capabilities of the Respond Analyst to include endpoint system details identifying incidents related to suspect activity from binaries, client apps, PowerShell and other suspicious entities.

Combining EDR analysis with insights from network intrusion detection, web filtering and other network telemetries, the Respond Analyst extends its already comprehensive coverage. This allows security operations centers to increase visibility, efficiency and effectiveness, thereby reducing false positives and increasing the probability of identifying true malicious and actionable activity early in the attack cycle.

Recommended: AiThority Interview with Josh Poduska, Chief Data Scientist at Domino Data Lab

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Respond Software Unlocks the Value in EDR Data with Robotic Decision - AiThority

Company Continues to Deliver Access to Leading Edge Technologies Through Partnership Program

LONDON andAUSTIN, Texas, April 14, 2020 /PRNewswire/ --Blue Prism(AIM: PRSM) continues to provide easy access to the latest and most innovative technologies that accelerate and scale automation projects via the company'sDigital Exchange (DX), an intelligent automation "app store" and online community. To date, Blue Prism DX assets have been downloaded tens of thousands of times, making it the ideal online community foraugmenting and extending traditional RPA deployments.

Blue Prism logo (PRNewsfoto/Blue Prism)

Every month new Blue Prism affiliate Technology Alliance Program (TAP) partners add their intelligent automation capabilities to the DX, augmenting and extending the power of Blue Prism. Companies like Equinixare using assets found on the DX to streamline business processes for its accounts payable team resulting in a projected up to 7,000 hours returned to business annually and a 60 percent reduction in supplier query response times (from 1 week to 2 days).

This month innovators like Artificial Solutions, CLEVVAand TAIGERhave joined the Blue Prism DX making their software accessible to all. The latest capabilities on the DX enable organizations to take advantage of conversational AI applications, front-office automationsas well as gaining insights from unstructured data such as emails, chat transcripts, outbound marketing materials, internal memos and legal documents, in a way that hasn't been previously possible.

"The Blue Prism DX community is a game changer because it enables, augments and extends our Digital Workforce capabilities with drag-and-dropease of use," says Linda Dotts, SVP Global Partner Strategy and Programs for Blue Prism. "It provides easy access to the latest innovations in intelligent automation through search and an a la carte menu of options. Our Technology Alliance Partners provide easy access to their software integrations via the DX, so everyone can drive better business outcomes via their Digital Workforce."

Below is a quick summary of the new capabilities being brought to market by these new TAP affiliate partners:

Artificial Solutions enables customers of Blue Prism to extend their RPA implementationsthrough an intelligent conversational interface, all the way from consumer engagement to process execution and resolution. This runs the gamut from answering a simple billing query or booking a reservation, to delivering advice on complex topics or resetting a home automation task, an intelligent conversational interface delivers a more human-like, natural experience.

The company's award-winning conversational AI platform Teneo allows intelligent conversational chatbots to integrate directly with Blue Prism's Digital Workers, providing a personalized interface that guides and supports end-users as they fill out data. Teneo runs across 36 languages, multiple platforms and channels, and the ability to analyze enormous quantities of conversational data is fully integrated, delivering new levels of customer insight.

"We're delighted to be working with Blue Prism and its customers helping them further extend the value of existing and new RPA implementations," says Andy Peart, Chief Marketing & Strategy Officer at Artificial Solutions. "Teneo's highly conversational capabilities deliver the accuracy needed to understand and respond in a near-humanlike way, improving efficiency for the client while giving users the seamless experience they're looking for."

Story continues

CLEVVAhelps customers realize straight-through processing across staff-assisted and digital self-service channels. This solution enables front office staff to navigate through rule-based decisions and actions, so they get it right, while driving intelligent self-service across any digital interface (website, mobile app, chatbot, social media, in store kiosk). The combination of CLEVVA's front office digital workers with Blue Prism allows customers to effectively automate end-to-end processes across multiple channels in a consistent, compliant and context-relevant way.

"We allow companies to capture the business logic that sits outside of operational systemsnormally residing in experts, knowledge bases and decision tree scriptsand place it into a digital worker," says Ryan Falkenberg, co-CEO and founder of CLEVVA. "By coupling our ability to navigate customer engagements with Blue Prism's ability to perform required system actions, we're making end-to-end process automation a reality."

TAIGERhelps customers drive greater efficiencies through the automation of complex cognitive tasks which require interpretation, understanding and judgment.By using semantic technology, TAIGER's AI software can read documents and accurately extract data points to automate business tasks, while helping customers make faster and better-informed business decisions.

"We are committed to scale our solution and forge strong global partnerships that bring about a new era of productivity for organizations," says Founder and CEO of TAIGER, Dr. Sinuhe Arroyo. "This partnership encourages us to keep pushing the boundaries of AI in our quest to achieve the best in man-machine collaboration."

Joining the TAP is easier than ever with a new self-serve function on Blue Prism's DX. To find out more please visit:https://digitalexchange.blueprism.com/site/global/partner/index.gsp.

About Blue PrismBlue Prism's vision is to provide a Digital Workforce for Every Enterprise. The company's purpose is to unleash the collaborative potential of humans, operating in harmony with a Digital Workforce, so every enterprise can exceed their business goals and drive meaningful growth, with unmatched speed and agility.

Fortune 500and public-sector organizations, among customers across 70 commercial sectors, trust Blue Prism's enterprise-grade connected-RPA platform, which has users in more than 170 countries. By strategically applying intelligent automation, these organizations are creating new opportunities and services, while unlocking massive efficiencies that return millions of hours of work back into their business.

Available on-premises, in the cloud, hybrid, or as an integrated SaaS solution, Blue Prism's Digital Workforce automates ever more complex, end-to-end processes that drive a true digital transformation, collaboratively, at scale and across the entire enterprise.

Visit http://www.blueprism.comto learn more or follow Blue Prism on Twitter @blue_prism and on LinkedIn.

2020 Blue Prism Limited. "Blue Prism", "Thoughtonomy", the "Blue Prism" logo and Prism device are either trademarks or registered trademarks of Blue Prism Limited and its affiliates. All Rights Reserved.

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Latest AI, Machine Learning, Data Extraction and Workflow Optimization Capabilities Added to Blue Prism's Digital Workforce - Yahoo Finance

AI and ML are now common within most digital processes, but they bring faults as well as benefits when it comes to DevOps

AI and ML can be very beneficial, but they aren't without their faults.

Machine learning (ML), and artificial intelligence (AI) in general, has been commonly used within DevOps to aid developers and engineers with tasks.

The technology is highly capable of speeding tasks up and getting them done around the clock without its human colleagues needing to be present, if it is trained properly.

It is here where problems with AI and ML implementation can occur; if not taught properly, AI can display some kind of bias, and successful deployment of new software isnt always a guarantee.

Add these possible issues to the challenge of getting staff on board with AI and ML implementation for the first time, and the relationship between this technology and DevOps may not always be the perfect match. With this in mind, lets take a look at some pros and cons.

One common use case of AI and ML is to provide context to the various types of data a company has at its disposal. AI can be taught to categorise data according to its purpose quicker than engineers can.

This is a vital part of DevOps due to engineers needing to carefully examine code releases in order to ensure successful software deployment.

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AI and ML will be essential to aiding developers in making sense of the information housed across various data warehouses, said Kevin Dallas, CEO of Wind River. In fact, we believe it will become mandatory for analysing and processing data, as humans simply wont be able to do it themselves.

It will enable developers to better understand and use the data at hand; for example, to understand not just the error, or the occurrence of a fault, but the detail of what happened in the run up to the fault.

Whats clear is that AI/ML is a vital strategy decision for every form of data from management and diagnostics to business-based value insights.

A major part of DevOps is ensuring that all possible errors are quickly eradicated before new software is deployed and made available to end users.

Joao Neto, head of quality & acceleration at OutSystems, explained: With the right data, AI/ML can help us analyse our value streams and help us identify bottlenecks. It can detect inefficiencies and even alert or propose corrective actions.

Smarter code analysis tools and automatic code-reviews will help us detect severe issues earlier and prevent them from being propagated downstream. Testing tools will also become smarter, helping developers identify test coverage gaps and defects.

We can easily extrapolate similar benefits to other practices and roles like security, architecture, performance, and user experience.

Neto continued by explaining the benefits of AI and ML in experimentation when it comes to DevOps.

Running experiments is not trivial and typically requires specialised skills that most teams dont have, such as data analysts, he said.

Picking adequate control groups and understanding if your data is statistically relevant is literally a science. AI/ML can help democratise experimentation and make it accessible to all software teams, maybe even to business roles.

We can also anticipate that by combining observability with ML techniques. Teams can understand and learn how their customers are using the product, what challenges customers face, and what specific situations lead to system failure.

Clint Hook, director of Data Governance at Experian, looks at how organisations can automate data quality to support artificial intelligence and machine learning. Read here

Its clear that AI and ML has an array of capabilities for benefitting DevOps processes, especially when carrying out analysis in the back end.

However, when it comes to deployment, developers and engineers may need to think more specifically about where its needed, as working with AI here may not turn out perfect every time.

A lot of AI projects have been struggling, not so much with the back end analysis such as the building of predictive models, but more with the issue of how to deploy these assets into production, said Peter van der Putten, director of AI systems at Pegasystems.

To some extent good old DevOps practices can come to the rescue here, such as automated testing, integration and deployment pipelines.

But there are also requirements specific to deploying AI assets, for example the integration of ethical bias checks, or checks on whether the models to be deployed pass minimal transparency and explainability requirements for the use case at hand.

Brian Carpenter, senior director, technology strategy, Pure Storage, discusses why enterprises need to modernise their IT infrastructure if they wish to succeed with AI. Read here

A criticism that has been made towards AI in DevOps is that it can distract engineering teams from the end goal, and from more human elements of processes that are just as vital to success.

When it comes to tech and DevOps, were not talking about strong AI or Artificial General Intelligence that mimics the breadth of human understanding, but soft, or weak AI, and specifically narrow, task-specific intelligence, saidNigel Kersten, field CTO of Puppet. Were not talking about systems that think, but really just referring to statistics paired with computational power being applied to a specific task.

Now that sounds practical and useful, but is it useful to DevOps? Sure, but I strongly believe that focusing on this is dangerous and distracts from the actual benefits of a DevOps approach, which should always keep humans front and centre.

I see far too many enterprise leaders looking to AI and Robotic Process Automation as a way of dealing with the complexity and fragility of their IT environments instead of doing the work of applying systems thinking, streamlining processes, creating autonomous teams, adopting agile and lean methodologies, and creating an environment of incremental progress and continuous improvement.

Focus on maximising the value of the intelligence your human employees have first before you start looking to the robots for answers. Once youve done that, look to machine learning and statistics to augment your people, automating away even more of their soul-crushing work in narrow domains such as anomaly detection.

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While AI and ML has proven to be successful in speeding up DevOps as well as other areas of digital strategies, AI as a whole may need more time to develop and improve.

As this continues to be worked on by developers, what does the future hold for this technologys relationship with DevOps?

As the application of AI and ML in DevOps grows, well increasingly see companies benefit and drive value for the business from more real-time insights, whereby AI and ML frameworks deployed on active systems will be able to optimise the system based on real-time development, validation and operational data, said Dallas.

These are the digital transformation conversations weve been having with customers across the industries we serve. Companies are realising that they cant do things the traditional way and expect to get the type of results that the new world is looking for.

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The pros and cons of AI and ML in DevOps - Information Age

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