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Qualisense uses Machine Learning to help companies enhance software development strategies, streamline testing and reduce the costs of ensuring software quality

Qualitest, the worlds largest software testing and quality assurance company, announces the global launch of its new Qualisense suite of innovative Machine Learning-powered tools and services. The newly launched Qualisense toolkit is the next iteration of the previous Qualisense Test Predictor service.

With this launch, Qualisense becomes a standalone product-set providing a 360-approach to Machine Learning that will help companies optimise software development by enabling swifter, more accurate and higher quality software testing approaches, while being agnostic to their development tools and technology.

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Increasingly, companies are employing an iterative approach to software development deploying smaller updates rapidly rather than slowly delivering larger revisions. The use of Qualisense will optimise testing and quality delivery, and remove bottlenecks and barriers to sophisticated multi-phased software deployments, reducing the need for certain tests, and ultimately making quality engineers more efficient, redistributing key resources and providing user-friendly interfaces.

Incorporating breakthrough automation and Machine Learning-poweredsoftware testing models, Qualisenses suite will allow companies to enhance risk-based testing protocols. Similarly, by increasing both accuracy and speed of the continuous delivery of new software, client processes will run more efficiently, strategically and cost-effectively critical to the software industry at large.

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The software testing industry grew 14% in 2019, with quality engineering being regarded as a key factor for successful software deployments. Earlier this year, Gartner named Qualitest a Visionary in this space for the consecutive sixth year, further strengthening its position in the market. 2019 saw Qualitest expand its operations inRomania,India,Israel, the US and move its corporate headquarters toLondonto better expand throughout EMEA with an acquisition of AI company AlgoTrace inDecember 2019.

Ron Ritter, Head of AI and Data Science at Qualitest, said:Qualisense will enable us to better streamline the unique testing needs of our clients. Incorporating AI into the testing process has already proven essential to ensuring the provision of swift, accurate and reliable software deployments. The new technical capabilities that Qualisense offers our company, global client base and the industry more widely are endless.

Norm Merritt, CEO of Qualitest, said:Testing was once something that was done at the end of the software development process, however with the advances in testing methodologies, we have been able to entrench it earlier within the process, making it more accurate, quicker, and more effective. Expanding the Qualisense toolkit will allow our clients to embrace best practice quality engineering, and ensure that Qualitest remains on the cutting-edge of software testing methodologies.

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Qualitest Announces Global Launch of Qualisense, to Expand AI-Powered Software Testing - AiThority

Trusted Business Insights answers what are the scenarios for growth and recovery and whether there will be any lasting structural impact from the unfolding crisis for the Machine Learning market.

Trusted Business Insights presents an updated and Latest Study on Machine Learning Market 2019-2026. The report contains market predictions related to market size, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market.The report further elaborates on the micro and macroeconomic aspects including the socio-political landscape that is anticipated to shape the demand of the Machine Learning market during the forecast period (2019-2029).It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary, and SWOT analysis.

Get Sample Copy of this Report @ Machine Learning Market Size, Share, Global Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

Industry Insights, Market Size, CAGR, High-Level Analysis: Machine Learning Market

The global machine learning market size was valued at USD 6.9 billion in 2018 and is anticipated to register a CAGR of 43.8% from 2019 to 2025. Emerging technologies such as artificial intelligence are changing the way industries and humans work. These technologies have optimized supply chains, launched various digital products and services, and transformed overall customer experience. Various tech firms are investing in this filed to develop AI platforms, while various startups are focusing on niche domain solutions. With this rapid development, AI techniques such as machine learning are gaining significant traction in the market.Machine learning is a subset of artificial intelligence. The concept has evolved from computational learning and pattern recognition in artificial intelligence. It explores the construction and study of algorithms and carries out forecasts on data. The applications of machine learning include e-mail filtering, Optical Character Recognition (OCR), detection of network intruders, computer vision, and learning to rank.

The technology has paved the way across various applications. In advertising, this technology is used to predict the behavior of a customer and helps in improving advertising campaigns. AI-driven marketing uses various models to optimize, automate, and augment the data into actions. In the case of banking and finance, loan approval, assets management, and other processes are carried out using machine learning. Other applications, such as security, document management, and publishing, are also using this technology, thereby driving the market.Recently, machine learning has made its way into new aspects. For instance, the U.S. Army is planning to use this technology in combat vehicles for predictive maintenance. It will help in determining repair and service required in these vehicles with details such as when and where the repair is required. The stock market is also making use of this technology in market prediction with an accuracy level of approximately 60%.

Component Insights of Machine Learning Market

Based on component, the market is divided into hardware, software, and services. The hardware segment is expected to register the highest CAGR over the forecast period. This can be attributed to growing adoption of hardware optimized for machine learning. Development of customized silicon chips with AI and ML capabilities is driving the adoption of hardware. Development of more powerful processing devices by companies such as SambaNova Systems are anticipated to further drive the market.The software segment is expected to account for a moderate share in the market. The adoption of cloud-based software is anticipated to rise due to enhanced cloud infrastructure and hosting parameters. Cloud-based software allows users to move from machine to deep learning, thereby driving adoption. Demand for machine learning services has been on a rise in recent years. Managed services help customers manage their ML tools and deal with varied dependency stacks.Enterprise Size InsightsBased on enterprise size, the machine learning market is categorized into Small and Medium Enterprises (SMEs) and large enterprises. The large enterprise segment accounted for the leading share in the market in 2018. This is due to increasing adoption of technologies such as artificial intelligence and data science to inject predictive insights into business operations. Large organizations are focusing on harnessing deep learning, machine learning, and optimization of decisions in order to deliver high business value.The adoption of machine learning is rapidly increasing among small and medium-sized enterprises. This is owing to easy and cost-effective deployment offered by machine learning. Availability of deployment options such as on cloud, on-premise, or hybrid allows SMEs to easily scale up their growing pilot projects and artificial intelligence initiatives, eliminating the need for large up-front investments.End-use InsightsBased on end use, the market is categorized into BFSI, healthcare, retail, law, advertising and media, agriculture, manufacturing, automotive and transportation, and others. While advertising and media held the leading share in 2018, the healthcare sector is expected to surpass this segment to account for the largest share by the end of the forecast period. This is due to rising adoption of this technology in emerging healthcare areas. For instance, this technology is being used to predict the probability of death of a person. Use of machine learning for quantitative insights for better diagnosis and using it to prevent diseases is moving the field of medicine from reactive to proactive and this is poised to drive the market.

The law segment is expected to register the highest CAGR over the forecast period. This is due to rising adoption of machine learning algorithms across various legal applications. In case of litigation, ML is used for continuous active learning for the process of document review. Due diligence analysis in the merger and acquisition process is done using ML. Privacy, information governance, expert systems, and client collaboration are some of the emerging legal areas that are adopting machine learning.

Regional Insights of Machine Learning Market

The market in North America held the dominant share in 2018, thanks to numerous banking organizations in the region investing in ML-based firms. For instance, in November 2019, JPMorgan Chase & Co. announced its investment in Limeglass, a provider of AI, ML, and NLP to analyze institutional research. The latter company is expected to assist emerging technology companies in developing various products required for banking.Asia Pacific is anticipated to register the highest CAGR over the forecast period. This is due to growing adoption of machine learning in emerging markets with a massive talent base, such as India. Greater access to consumers who are willing to try AI-enabled services and products is further driving the regional market. In May 2018, NITI Aayog, a policy think tank of the Government of India, collaborated with Google LLC, a multinational technology company. Through this collaboration, the former company will incubate and train start-ups based on AI in India.

Market Share Insights of Machine Learning Market

Key industry participants include Amazon Web Services, Inc.; Baidu Inc.; Google Inc.; H2O.ai; Intel Corporation; International Business Machines Corporation; Hewlett Packard Enterprise Development LP; Microsoft Corporation; SAS Institute Inc.; and SAP SE. Several vendors are entering into partnerships with end-use industries to enhance their reach. For instance, Microsoft Corporation partnered with LV Prasad Eye Institute in Hyderabad. This partnership is aimed at enabling machine learning to bring data-driven eye care services in India. Vendors are also focusing on launching new products in the market. For instance, International Business Machines Corporations machine learning technology advances the early detection of diabetic eye disease using deep learning.

Segmentations, Sub Segmentations, CAGR, & High-Level Analysis overview of Machine Learning Market Research ReportThis report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2014 to 2025. For the purpose of this study, this market research report has segmented the global machine learning market report based on component, enterprise size, end use, and region:

Component Outlook (Revenue, USD Million, 2019 2030)

Hardware

Software

Services

Enterprise Size Outlook (Revenue, USD Million, 2019 2030)

SMEs

Large Enterprises

End-use Outlook (Revenue, USD Million, 2019 2030)

Healthcare

BFSI

Law

Retail

Advertising & Media

Automotive & Transportation

Agriculture

Manufacturing

Others

Quick Read Table of Contents of this Report @ Machine Learning Market Size, Share, Global Market Research and Industry Forecast Report, 2025 (Includes Business Impact of COVID-19)

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How COVID-19 Pandemic Will Impact Machine Learning Market Business Opportunity, And Growth 2020-2026 - Jewish Life News

The global Machine Learning market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning market.

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A perfect mix of quantitative & qualitative Machine Learning market information highlighting developments, industry challenges that competitors are facing along with gaps and opportunities available and would trend in Machine Learning market. The study bridges the historical data from 2014 to 2019 and estimated until 2025.

Leading players of the global Machine Learning market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning market. It also provides useful recommendations for new as well as established players of the global Machine Learning market.

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A major chunk of this Global Machine Learning Market research report is talking about some significant approaches for enhancing the performance of the companies. Marketing strategies and different channels have been listed here. Collectively, it gives more focus on changing rules, regulations, and policies of governments. It will help to both established and new startups of the market.

In conclusion, the Machine Learning Market report is a reliable source for accessing the research data that is projected to exponentially accelerate your business. The report provides information such as economic scenarios, benefits, limits, trends, market growth rate, and figures. SWOT analysis is also incorporated in the report along with speculation attainability investigation and venture return investigation.

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Adroit Market Research is an India-based business analytics and consulting company. Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a markets size, key trends, participants and future outlook of an industry. We intend to become our clients knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

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Machine Learning Market 2019 Industry Analysis By Size, Share, Growth, Key-Companies, Trends, Demand, Future Prospects and Forecast Till 2025 - Cole...

We have heard of CPUs and TPUs, now, NVIDIA with the help of its recent acquisition Mellanox is bringing a new class of processors to power up deep learning applications DPUs or data processing units.

DPUs or Data Processing Units, originally popularised by Mellanox, now wear a new look with NVIDIA; Mellanox was acquired by NVIDIA earlier this year. DPUs are a new class of programmable processor that consists of flexible and programmable acceleration engines which improve applications performance for AI and machine learning, security, telecommunications, storage, among others.

The team at Mellanox has already deployed the first generation of BlueField DPUs in leading high-performance computing, deep learning, and cloud data centres to provide new levels of performance, scale, and efficiency with improved operational agility.

The improvement in performance is due to the presence of high-performance, software programmable, multi-core CPU and a network interface capable of parsing, processing, and efficiently transferring data at line rate to GPUs and CPUs.

According to NVIDIA, a DPU can be used as a stand-alone embedded processor. DPUs are usually incorporated into a SmartNIC, a network interface controller. SmartNICs are ideally suited for high-traffic web servers.

A DPU based SmartNIC is a network interface card that offloads processing tasks that the system CPU would normally handle. Using its own on-board processor, the DPU based SmartNIC may be able to perform any combination of encryption/decryption, firewall, TCP/IP and HTTP processing.

The CPU is for general-purpose computing, the GPU is for accelerated computing and the DPU, which moves data around the data centre, does data processing.

These DPUs are known by the name of BlueField that have a unique design that can enable programmability to run at speeds of up to 200Gb/s. The BlueField DPU integrates the NVIDIA Mellanox Connect best-in-class network adapter, encompassing hardware accelerators with advanced software programmability to deliver diverse software-defined solutions.

Organisations that rely on cloud-based solutions, especially can benefit immensely from DPUs. Here are few such instances, where DPUs flourish:

Bare metal environment is a network where a virtual machine is installed

The shift towards microservices architecture has completely transformed the way enterprises ship applications at scale. Applications that are based on the cloud have a lot of activity or data generation, even for processing a single application request. According to Mellanox, one key application of DPU is securing the cloud-native workloads.

For instance, Kubernetes security is an immense challenge comprising many highly interrelated parts. The data intensity makes it hard to implement zero-trust security solutions, and this creates challenges for the security team to protect customers data and privacy.

As of late last year, the team at Mellanox stated that they are actively researching into various platforms and integrating schemes to leverage the cutting-edge acceleration engines in the DPU-based SmartNICs for securing cloud-native workloads at 100Gb/s.

According to NVIDIA, a DPU comes with the following features:

Know more about DPUs here.

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What Are DPUs And Why Do We Need Them - Analytics India Magazine

Historians interested in the way events and people were chronicled in the old days once had to sort through card catalogs for old papers, then microfiche scans, then digital listings but modern advances can index them down to each individual word and photo. A new effort from the Library of Congress has digitized and organized photos and illustrations from centuries of news using state of the art machine learning.

Led by Ben Lee, a researcher from the University of Washington occupying the Librarys Innovator in Residence position, the Newspaper Navigator collects and surfaces data from images from some 16 million pages of newspapers throughout American history.

Lee and his colleagues were inspired by work already being done in Chronicling America, an ongoing digitization effort for old newspapers and other such print materials. While that work used optical character recognition to scan the contents of all the papers, there was also a crowdsourced project in which people identified and outlined images for further analysis. Volunteers drew boxes around images relating to World War I, then transcribed the captions and categorized the picture.

This limited effort set the team thinking.

I loved it because it emphasized the visual nature of the pages seeing the visual diversity of the content coming out of the project, I just thought it was so cool, and I wondered what it would be like to chronicle content like this from all over America, Lee told TechCrunch.

He also realized that what the volunteers had created was in fact an ideal set of training data for a machine learning system. The question was, could we use this stuff to create an object detection model to go through every newspaper, to throw open the treasure chest?

The answer, happily, was yes. Using the initial human-powered work of outlining images and captions as training data, they built an AI agent that could do so on its own. After the usual tweaking and optimizing, they set it loose on the full Chronicling America database of newspaper scans.

It ran for 19 days nonstop definitely the largest computing job Ive ever run, said Lee. But the results are remarkable: millions of images spanning three centuries (from 1789 to 1963) and organized with metadata pulled from their own captions. The team describes their work in a paper you can read here.

Assuming the captions are at all accurate, these images until recently only accessible by trudging through the archives date by date and document by document can be searched for by their contents, like any other corpus.

Looking for pictures of the president in 1870? No need to browse dozens of papers looking for potential hits and double-checking the contents in the caption just search Newspaper Navigator for president 1870. Or if you want editorial cartoons from the World War II era, you can just get all illustrations from a date range. (The team has already zipped up the photos into yearly packages and plans other collections.)

Here are a few examples of newspaper pages with the machine learning systems determinations overlaid on them (warning: plenty of hat ads and racism):

Thats fun for a few minutes for casual browsers, but the key thing is what it opens up for researchers and other sets of documents. The team is throwing a data jam today to celebrate the release of the data set and tools, during which they hope to both discover and enable new applications.

Hopefully it will be a great way to get people together to think of creative ways the data set can be used, said Lee. The idea Im really excited by from a machine learning perspective is trying to build out a user interface where people can build their own data set. Political cartoons or fashion ads, just let users define theyre interested in and train a classifier based on that.

A sample of what you might get if you asked for maps from the Civil War era.

In other words, Newspaper Navigators AI agent could be the parent for a whole brood of more specific ones that could be used to scan and digitize other collections. Thats actually the plan within the Library of Congress, where the digital collections team has been delighted by the possibilities brought up by Newspaper Navigator, and machine learning in general.

One of the things were interested in is how computation can expand the way were enabling search and discovery, said Kate Zwaard. Because we have OCR, you can find things it would have taken months or weeks to find. The Librarys book collection has all these beautiful plates and illustrations. But if you want to know like, what pictures are there of the Madonna and child, some are categorized, but others are inside books that arent catalogued.

That could change in a hurry with an image-and-caption AI systematically poring over them.

Newspaper Navigator, the code behind it and all the images and results from it are completely public domain, free to use or modify for any purpose. You can dive into the code at the projects GitHub.

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Millions of historic newspaper images get the machine learning treatment at the Library of Congress - TechCrunch