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Global Machine Learning-as-a-Service (MLaaS) Market SWOT Analysis | Growth Analysis Research Report 2020 | Top Key players update, COVID-19 impact analysis and Forecast 2025

Our latest research report entitled Global Machine Learning-as-a-Service (MLaaS) Market report 2020-2025 provides comprehensive and deep insights into the market dynamics and growth of Machine Learning-as-a-Service (MLaaS). The latest information on market risks, industry chain structure, Machine Learning-as-a-Service (MLaaS) cost structure, and opportunities are offered in this report. The entire industry is fragmented based on geographical regions, a wide range of applications, and Machine Learning-as-a-Service (MLaaS) types. The past, present, and forecast market information will lead to investment feasibility by studying the crucial Machine Learning-as-a-Service (MLaaS) growth factors. The SWOT analysis of leading Machine Learning-as-a-Service (MLaaS) players (SAS Institute Inc., Google LLC, Hewlett Packard Enterprise Development LP, Artificial Solutions)will help the readers in analyzing the opportunities and threats to the market development.

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NOTE: Global Machine Learning-as-a-Service (MLaaS) report can be customized according to the users requirements.

Top Leading Players covered in this Report:

Initially, the report illustrates the fundamental overview of Machine Learning-as-a-Service (MLaaS) on basis of the product description, classification, cost structures, and type. The past, present, and forecast Machine Learning-as-a-Service (MLaaS) market statistics are offered. The market size analysis is conducted on the basis of Machine Learning-as-a-Service (MLaaS) market concentration, value and volume analysis, growth rate, and emerging market segments.

A complete view of the Machine Learning-as-a-Service (MLaaS) industry is provided based on definitions, product classification, applications, major players driving the global Machine Learning-as-a-Service (MLaaS) market share and revenue. The information in the form of graphs, pie charts will lead to an easy analysis of an industry. The market share of top leading companies, their plans, and business policies, growth factors will help other players in gaining useful business tactics.

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The foremost regions analyzed in this study include North America (United States, Canada, Mexico, and Others), Europe (Germany, France, Russia, Italy, Netherlands, and Others), South America (Columbia, Brazil, Argentina, and Others), Asia-Pacific (China, Japan, Korea, India, and Others), Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Others) and rest of the world.

On the basis of Types, the Machine Learning-as-a-Service (MLaaS) market is primarily split into,

On the basis of applications, the Machine Learning-as-a-Service (MLaaS) market is primarily split into,

If you have any questions Or you need any customization in the report? Make an inquiry here:https://www.reportspedia.com/report/technology-and-media/2020-2025-global-machine-learning-as-a-service-(mlaas)-market-reportproduction-and-consumption-professional-analysis-(impact-of-covid-19)/79118#inquiry_before_buying

Comprehensive research methodology which drives the Machine Learning-as-a-Service (MLaaS) market statistics can be structured as follows:

The leading Machine Learning-as-a-Service (MLaaS) players, their company profile, growth rate, market share, and global presence are covered in this report. The competitive Machine Learning-as-a-Service (MLaaS) scenario on the basis of price and gross margin analysis is studied in this report. All the key factors like consumption volume, price trends, market share, import-export details, manufacturing capacity are included in this report. The forecast market information will lead to strategic plans and an informed decision-making process. The emerging Machine Learning-as-a-Service (MLaaS) market sectors, mergers, and acquisitions, market risk factors are analyzed. Lastly, the research methodology and data sources are presented

Segment 1, states the objectives of Machine Learning-as-a-Service (MLaaS) market, overview, introduction, product definition, development aspects, and industry presence;

Segment 2, elaborates the Machine Learning-as-a-Service (MLaaS) market based on key players, their market share, sales volume, company profiles, Machine Learning-as-a-Service (MLaaS) competitive market scenario, and pricing

Segment 3, analyzes the Machine Learning-as-a-Service (MLaaS) market at a regional level based on sales ratio and market size from 2015 to 2019;

Segment 4, 5, 6 and 7, explains the Machine Learning-as-a-Service (MLaaS) market at the country level based on product type, applications, revenue analysis;

Segment 8 and 9, states the Machine Learning-as-a-Service (MLaaS) industry overview during past, present, and forecast period from 2020 to 2025;

Segment 10 and 11, describes the market status, plans, expected growth based on regions, type and application in detail for a forecast period of 2020-2025;

Segment 12, covers the marketing channels, dealers, manufacturers, traders, distributors, consumers of Machine Learning-as-a-Service (MLaaS).

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Global Machine Learning-as-a-Service (MLaaS) Market Development Strategy, Manufacturers Analysis, COVID-19 impact, and Forecast 2020-2025 The Bisouv...

The telecommunication networks of the future will not only have to support millions of 4G and 5G subscribers, but must also manage a huge number of connected internet-of-things (IoT) devices. With the need to meet exponentially growing data and signalling requirements, a new approach is needed to cope with the unpredictable and surging demands placed on modern networks.

Jio Platforms, a subsidiary of Reliance Industries, turned to machine learning to autonomously manage its large communication infrastructure. With a modest budget of $1m, Jio Platforms designed and implemented Atom, an artificial intelligence-based platform, from scratch within 12 months to process more than 500 billion records a day.

At its heart, Atom, which helped Jio Platforms clinch the telecoms category in the Computer Weekly Innovation Awards APAC, is a disaggregated data lake platform tailored to enable smarter network operations using machine learning.

Atom an acronym for Adaptive Troubleshooting, Operations and Management was designed to collect and process a massive volume of network-centric statistics and events. The goal was to proactively detect anomalous network patterns and facilitate root-cause analysis and resolution before network problems even impact operations.

Jio Platforms said Atom provides code-free operational insights, data binding and correlation. Built with automated service-level agreement (SLA) management capabilities in the workflow engine, it orchestrates operational tasks between systems for organisational transparency.

It can also offer instant notifications and live data tracking from the vast amount of data collected using virtual probes and various network functions. This is made possible by a data ingestion engine designed to process billions of documents. Immediate action therefore becomes possible, as opposed to the traditional approach of only reacting to problems.

The Atom platform provides multiple ways to create reports and dashboards on the fly. Detection includes comparisons with baseline data and monitoring of operational metrics. Once a relevant condition is identified, the system analyses the data by correlating, searching for errors, or deriving the real context of the erroneous scenario.

But why did Jio Platforms begin building this first-of-its-kind system instead of relying on a suitable commercial solution? The company said it has always worked to reduce dependence on external providers and cited the cost-related advantages of developing an in-house solution that relies on software running on standard servers. Indeed, because Atom avoids the use of proprietary probes, vendor dependencies were also eliminated on that front.

Building the entire system in-house meant Jio Platforms could focus on innovation and adopt tried-and-true practices, such as developing an open solution that interoperates well with third-party systems. Atom conforms with various standards from the European Telecommunications Standards Institute and 3GPP and has the versatility to support network functions from the edge, core, on the various layers of the IP stack, and IoT applications.

Because crucial software components are developed from the ground up, the team could incorporate high-performance considerations and state-optimised designs for application resilience from the start. Jio Platforms said Atom has real-time analytics capabilities to process 50 million records every second, as well as a record capacity of over 10 trillion with support for more than 100PB of storage.

The platform has unique anomaly detection capabilities that can drill down to individual end-nodes, whether a physical server, virtual machine or containerised service, to precisely identify problematic elements within the network.

Also, the system can understand and correlate counters and logs from the radio access network (RAN) and other systems to identify the causes of failure and take corrective actions.

Telecommunications companies operate with very large network infrastructure with large volumes of data traffic, said the team. Processing and analysing this data with the help of scientific algorithms, methodologies and tools is the need of the hour.

It was with this in mind that Jio Platforms built Atom to enable actionable intelligence from network data in real time.

Continuous demand for scaling the telecom network is to be expected over the next few years as the colossal data volumes driven by 5G become a reality. More than ever, operational procedures will have to be automated to meet the ever-growing needs of modern networks and for telecommunication firms to stay relevant.

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CW Innovation Awards: Jio Platforms taps machine learning to manage telco network - ComputerWeekly.com

NASHVILLE, Tenn.--(BUSINESS WIRE)--Startup inSearchX and advisory services firm Strategic Vision announce they have partnered to deliver an exciting vehicle-matching technology that pairs new vehicle shoppers with their ideal vehicle match. To consult their vehicle "matchmaker," shoppers can AskOtto.

AskOtto is an interactive, anonymous communication platform that aligns your mobility needs and preferences with just a few simple questions, based on an analysis of millions of vehicle owners who have completed surveys for Strategic Vision since 1994. Strategic Vision specializes in understanding human decision-making processes through ValueCentered psychology, which connects product or service attributes to the underlying ValueEmotions that drive all decisions. In testing, shoppers found the quiz not only included their current vehicle, but also others they were already interested in as well as new, intriguing matches. One recent user described the quiz results as uncanny and asked what magic powers this technology?

We are very excited about the auto quiz we have developed with Strategic Vision; consumers today struggle with growing complexity of vehicle choices. says Eric Brown, CEO of inSearchX, and creator of AskOtto. By utilizing the millions of consumer vehicle experience studies conducted by Strategic Vision the mystery of finding the best car fit is resolved.

This powerful combination of psychological insights and cutting-edge technology creates a valuable tool that cuts through an increasingly complicated automotive landscape to connect new car shoppers with the best vehicle for their physical and emotional needs. Strategic Vision specializes in measuring what customers Love. explains Strategic Vision President Alexander Edwards, Taking that experience and putting it in the hands of the customer to help make a difficult process easier just makes sense.

After a shopper receives their top matches, they can use AskOtto to search local dealers' inventory near them. If they have any questions about a vehicle or offer, AskOtto connects them anonymously to a local dealer sales team who can provide timely answers.

To find your perfect vehicle match, visit: http://www.askotto.com/quiz

ABOUT INSEARCHX: inSearchX has developed an Open Dialog Advertising ODA Platform, branded as AskOtto. The AskOtto ODA platform is utilized by media companies, advertising agencies and other automotive marketers including local dealerships and national manufacturers to optimize advertising performance and the consumer retail experience. The platform provides consumers a one-click vehicle discovery and anonymous communication resource to find the perfect vehicle match and communicate with local car dealers remotely and privately. Visit http://www.insearchx.com, or email info@insearchx.com for more information.

ABOUT STRATEGIC VISION: Strategic Vision has spent decades helping companies understand human behavior and decision-making patterns in any field. By connecting product or service experience to the ValueEmotions that drive all decision-making, Strategic Vision connects the rational and emotional to understand customer advocacy, commitment, and loyalty. Please visit http://www.strategicvision.com for more information.

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inSearchX Partners with Strategic Vision's Using Machine Learning to Help Customers Find their Ideal Car With Uncanny Accuracy - Business Wire

According to a new study, Drug Ranking Using Machine Learning (DRUML) can accurately rank cancer therapies by efficacy across a range of cancer types.

Researchers from Queen Mary University of London, UK, have developed a machine learning algorithm that ranks drugs based on their efficacy in reducing cancer cell growth. According to the developers of Drug Ranking Using Machine Learning (DRUML), in the future the approach could advance personalised therapies by enabling oncologists select the best drugs to treat individual cancer patients.

One of the problems in cancer treatment, is that different people respond differently to the same treatments. This is because, despite tumours being classified as the same type, they exhibit a huge amount of variation in their genetic makeup and characteristics between patients. The field of personalised medicine is attempting to address this issue by combining genetic insights with other clinical and diagnostic information to identify patterns that can allow clinicians to predict patient responses to therapies and select the most effective interventions.

The application of artificial intelligence and machine learning to biomedicine, as was done in the study by the Queen Mary University, is one method being used to promote the development and adoption of personalised medicine and transform how cancers are diagnosed and treated in the future.

DRUML was trained using datasets derived from proteomics and phosphoproteomics analyses of 48 leukaemia, oesophagus and liver cancer cell lines responding to over 400 drugs. Based on these results it produces ordered lists predicting which drug will be most effective at reducing cancer cell growth. The team verified the predictive accuracy of DRUML using data obtained from 12 other laboratories and a clinical dataset of 36 primary acute myeloid leukaemia samples.

According to the developers, one of the most important features of the method is that, as new drug are developed, it could be retrained to include them in its predictions as well.

Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, remarked: DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. These are exciting results because previous machine learning methods have failed to accurately predict drug responses in verification datasets and they demonstrate the robustness and wide applicability of our method.

The research was funded by The Alan Turing Institute, Medical Research Council, Barts Charity and Cancer Research UK.

The method was published in Nature Communications.

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Machine learning used to predict most effective cancer drugs - European Pharmaceutical Review

This article was originally published here

Biomed Eng Online. 2021 Mar 25;20(1):31. doi: 10.1186/s12938-021-00865-9.

ABSTRACT

INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task.

METHODS: Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB).

RESULTS AND DISCUSSION: The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifiers performance remained the same (AUC = 0.97).

CONCLUSIONS: Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present studys findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

PMID:33766046 | DOI:10.1186/s12938-021-00865-9

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Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in...