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http://galusaustralis.com/2020/02/486473/legaltech-artificial-intelligence-market-2019-technology-advancement-and-future-scope-casetext-inc-catalyst-repository-systems-ebrevia/

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LegalTech Artificial Intelligence Market 2019 Technology Advancement and Future Scope – Casetext Inc., Catalyst Repository Systems, eBREVIA – Galus Australis

LegalTech Artificial Intelligence Market 2019 Technology Advancement and Future Scope – Casetext Inc., Catalyst Repository Systems, eBREVIA – Galus Australis Galus Australis BusinessGeneral NewsHealthcareIndustryInternationalLifestyleSci-Tech Wednesday, February 26 2020 Trending Needle Counters Market Comprehensive Study by Companies Medline Industries, Boen Healthcare Skin Scrub Trays Market Comprehensive Study by Companies Medline Industries, BD, Deroyal Global Portable Handheld Electronic Game Machine Market Outlook and Business Insights 2020-2026: Apollo Games, Sony, Aristocrat Leisure, IGT Infectious Disease Testing Using PCR for IVD Market Comprehensive Study by Companies Thermo Fisher, BD, Roche Diagnostics Veterinary Dental X-ray Generators Market Comprehensive Study by Companies Planmeca, Midmark, Medicatech USA Veterinary Ophthalmoscopes Market Comprehensive Study by Companies Heine Optotechnik, Gowllands Limited Veterinary Holters Market Comprehensive Study by Companies Dextronix, Nasiff Associates Veterinary Test Strips Market Comprehensive Study by Companies Analyticon Biotechnologies, Heska Veterinary Fecal Filter Market Comprehensive Study by Companies Woodley Equipment, Jorgensen Laboratories Animal Identification Systems Market Comprehensive Study by Companies Alidma, AEG ID, Agrident, AllflexContact Home/General News/LegalTech Artificial Intelligence Market 2019 Technology Advancement and Future Scope – Casetext Inc., Catalyst Repository Systems, eBREVIA General NewsLegalTech Artificial Intelligence Market 2019 Technology Advancement and Future Scope – Casetext Inc., Catalyst Repository Systems, eBREVIA tanmay February 25, 2020 The report titled “LegalTech Artificial Intelligence Market” report will be very useful to get a stronger and effective business outlook. It provides an in-depth analysis of different attributes of industries such as trends, SWOT analysis, policies, and clients operating in several regions. The qualitative and quantitative analysis techniques have been used by analysts to provide accurate and applicable data to the readers, business owners and industry experts.The LegalTech Artificial Intelligence market was valued at 22500 Milion US$ in 2019 and is projected to reach 40700 Million US$ by 2025, at a CAGR of 3.0% during the forecast period.Legal technology, also known as Legal Tech, refers to the use of technology and software to provide legal services. Legal Tech companies are generally startups founded with the purpose of disrupting the traditionally conservative legal market. LegalTech Artificial Intelligence is the application of AI in Legal Tech area.Available discount (Exclusive Offer Flat 30%)Click the link to get a Sample Copy of the Report before purchase:https://www.marketinsightsreports.com/reports/06111286038/global-legaltech-artificial-intelligence-market-size-status-and-forecast-2019-2025/inquiry?Source=GA&Mode=47 The report presents the market competitive landscape and a corresponding detailed analysis of the major vendor/key players in the market. Top Companies in the Global LegalTech Artificial Intelligence Market: Blue J Legal, Casetext Inc., Catalyst Repository Systems, eBREVIA, Everlaw, FiscalNote, Judicata, Justia, Knomos Knowledge Management Inc., Lawgeex, Legal Robot Inc., LEVERTON, LexMachina, Loom Analytics, Luminance Technologies Ltd., Ravel Law and others.Global LegalTech Artificial Intelligence Market Split By Product Type and Applications:This report segments the global LegalTech Artificial Intelligence Market on the basis of Types are: Lawyers ClientsOn the basis of Application, the Global LegalTech Artificial Intelligence Market is segmented into: Document Management System Practice and Case Management Contract Management IP-Management Legal Research Legal Analytics Cyber Security Predictive Technology ComplianceInquire for Discount: https://www.marketinsightsreports.com/reports/06111286038/global-legaltech-artificial-intelligence-market-size-status-and-forecast-2019-2025/discount?Source=GA&Mode=47 Regional Analysis For LegalTech Artificial Intelligence Market:For the comprehensive understanding of market dynamics, the global LegalTech Artificial Intelligence Market is analysed across key geographies namely: United States, China, Europe, Japan, South-east Asia, India and others. Each of these regions is analysed on the basis of market findings across major countries in these regions for a macro-level understanding of the market.Important Features that are under Offering and Key Highlights of the Reports:– Detailed overview of LegalTech Artificial Intelligence Market. – Changing market dynamics of the LegalTech Artificial Intelligence Market industry. – In-depth segmentation of LegalTech Artificial Intelligence Market by Type, Application etc. – Historical, current and projected market size in terms of volume and value. – Recent industry trends and developments. – Competitive landscape of LegalTech Artificial Intelligence Market. – Strategies of key players and product offerings. – Potential and niche segments/regions exhibiting promising growth.Browse the report description and TOC:https://www.marketinsightsreports.com/reports/06111286038/global-legaltech-artificial-intelligence-market-size-status-and-forecast-2019-2025?Source=GA&Mode=47 We also offer customization on reports based on specific client requirement:1- Country level analysis for any 5 countries of your choice. 2- Competitive analysis of any 5 key market players. 3- 40 analyst hours to cover any other data pointsPlease connect with our sales team ([email protected]).Contact Us:Irfan Tamboli (Head of Sales) – Market Insights ReportsPhone: + 1704 266 3234 | +91-750-707-8687[email protected] | [email protected] TagsLegalTech Artificial Intelligence Industry LegalTech Artificial Intelligence Market LegalTech Artificial Intelligence Market analysis LegalTech Artificial Intelligence Market forecast LegalTech Artificial Intelligence Market trends Facebook Twitter Google+ LinkedIn StumbleUpon Tumblr Pinterest Reddit VKontakte Share via Email Print tanmayRelated Articles February 25, 2020 1 Automotive Suction Door Market Size, Status and Global Outlook 2020-2026 November 7, 2019 5 Palm Kernel Fatty Acid Diethanolamide Market Industry Scope, Future Expectations and Overview 2019 December 16, 2019 7 Solar Encapsulation Materials Market Size, Status and Precise Outlook 2019 to 2025 October 28, 2019 3 Special Fine Paper Market Analysis, Status and Business Outlook 2019 to 2025Recent News February 25, 2020Soft Magnetic Materials Market Size, Status, Global Outlook 2019 To 2025 February 25, 2020Adventure Tourism Market 2019 Business Strategies, Product Sales and Growth Rate, Assessment to 2025 February 25, 2020Millimeter Wave Technology Market Increasing Demand with Leading Player, Comprehensive Analysis, Forecast 2025 February 25, 2020Digital Pathology Market To Witness the Highest Growth Globally in Coming Years 2019-2025 February 25, 2020High Purity Alumina Market Survey Report 2019 Along with Statistics, Forecasts till 2025 February 25, 2020Ceramic Injection Molding Market Competitive Research And Precise Outlook 2019 To 2025 February 25, 2020Fertility Services Market Survey Report 2019 Along with Statistics, Forecasts till 2025 February 25, 2020Varicella Vaccine Market Competitive Research And Precise Outlook 2019 To 2025 February 25, 2020Regulatory Technology (RegTech) Market Research Technology Outlook 2020-2026 February 25, 2020Prefabricated Bathroom Pods Market Overview, Scope and Advancement Outlook Till 2026 Facebook Twitter WhatsApp Telegram Close Search for: Close Log In Forget? 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LegalTech Artificial Intelligence Market 2019 Technology Advancement and Future Scope

LegalTech Artificial Intelligence Market 2019 Technology Advancement Future Scope Casetext ., Catalyst Repository Systems, eBREVIA Galus Australis Galus Australis BusinessGeneral NewsHealthcareIndustryInternationalLifestyleSciTech Wednesday, February 26 2020 Trending Needle Counters Market Comprehensive Study Companies Medline Industries, Boen Healthcare Skin Scrub Trays Market Comprehensive Study Companies Medline Industries, BD, Deroyal Global Portable Handheld Electronic Game Machine Market Outlook Business Insights 20202026: Apollo Games, Sony, Aristocrat Leisure, IGT Infectious Disease Testing Using PCR IVD Market Comprehensive Study Companies Thermo Fisher, BD, Roche Diagnostics Veterinary Dental Xray Generators Market Comprehensive Study Companies Planmeca, Midmark, Medicatech USA Veterinary Ophthalmoscopes Market Comprehensive Study Companies Heine Optotechnik, Gowllands Limited Veterinary Holters Market Comprehensive Study Companies Dextronix, Nasiff Associates Veterinary Test Strips Market Comprehensive Study Companies Analyticon Biotechnologies, Heska Veterinary Fecal Filter Market Comprehensive Study Companies Woodley Equipment, Jorgensen Laboratories Animal Identification Systems Market Comprehensive Study Companies Alidma, AEG ID, Agrident, Allflex HomeGeneral NewsLegalTech Artificial Intelligence Market 2019 Technology Advancement Future Scope Casetext ., Catalyst Repository Systems, eBREVIA General NewsLegalTech Artificial Intelligence Market 2019 Technology Advancement Future Scope Casetext ., Catalyst Repository Systems, eBREVIA tanmay February 25, 2020 The report titled LegalTech Artificial Intelligence Market report useful stronger effective business outlook. It provides indepth analysis different attributes industries trends, SWOT analysis, policies, clients operating regions. The qualitative quantitative analysis techniques analysts provide accurate applicable data readers, business owners industry experts.The LegalTech Artificial Intelligence market valued 22500 Milion US 2019 projected reach 40700 Million US 2025, CAGR 3.0 forecast period.Legal technology, known Legal Tech, refers use technology software provide legal services. Legal Tech companies generally startups founded purpose disrupting traditionally conservative legal market. LegalTech Artificial Intelligence application AI Legal Tech area.Available discount Exclusive Offer Flat 30Click link Sample Copy Report purchase: The report presents market competitive landscape corresponding detailed analysis major vendorkey players market. Top Companies Global LegalTech Artificial Intelligence Market: Blue J Legal, Casetext ., Catalyst Repository Systems, eBREVIA, Everlaw, FiscalNote, Judicata, Justia, Knomos Knowledge Management ., Lawgeex, Legal Robot ., LEVERTON, LexMachina, Loom Analytics, Luminance Technologies Ltd., Ravel Law others.Global LegalTech Artificial Intelligence Market Split By Product Type Applications:This report segments global LegalTech Artificial Intelligence Market basis Types are: Lawyers ClientsOn basis Application, Global LegalTech Artificial Intelligence Market segmented into: Document Management System Practice Case Management Contract Management IPManagement Legal Research Legal Analytics Cyber Security Predictive Technology ComplianceInquire Discount: Regional Analysis For LegalTech Artificial Intelligence Market:For comprehensive understanding market dynamics, global LegalTech Artificial Intelligence Market analysed key geographies namely: United States, China, Europe, Japan, Southeast Asia, India others. Each regions analysed basis market findings major countries regions macrolevel understanding market.Important Features Offering Key Highlights Reports: Detailed overview LegalTech Artificial Intelligence Market. Changing market dynamics LegalTech Artificial Intelligence Market industry. Indepth segmentation LegalTech Artificial Intelligence Market Type, Application etc. Historical, current projected market size terms volume value. Recent industry trends developments. Competitive landscape LegalTech Artificial Intelligence Market. Strategies key players product offerings. Potential niche segmentsregions exhibiting promising growth.Browse report description TOC: We offer customization reports based specific client requirement:1 Country level analysis 5 countries choice. 2 Competitive analysis 5 key market players. 3 40 analyst hours cover data pointsPlease connect sales team [emailprotected]. Us:Irfan Tamboli Head Sales Market Insights ReportsPhone: 1704 266 3234 917507078687[emailprotected] [emailprotected] TagsLegalTech Artificial Intelligence Industry LegalTech Artificial Intelligence Market LegalTech Artificial Intelligence Market analysis LegalTech Artificial Intelligence Market forecast LegalTech Artificial Intelligence Markettrends Facebook Twitter Google LinkedIn StumbleUpon Tumblr Pinterest Reddit VKontakte Share Email Print tanmayRelated Articles February 25, 2020 1 Automotive Suction Door Market Size, Status Global Outlook 20202026 November 7, 2019 5 Palm Kernel Fatty Acid Diethanolamide Market Industry Scope, Future Expectations Overview 2019 December 16, 2019 7 Solar Encapsulation Materials Market Size, Status Precise Outlook 2019 2025 October 28, 2019 3 Special Fine Paper Market Analysis, Status Business Outlook 2019 2025Recent News February 25, 2020Soft Magnetic Materials Market Size, Status, Global Outlook 2019 To 2025 February 25, 2020Adventure Tourism Market 2019 Business Strategies, Product Sales Growth Rate, Assessment 2025 February 25, 2020Millimeter Wave Technology Market reasing Demand Leading Player, Comprehensive Analysis, Forecast 2025 February 25, 2020Digital Pathology Market To Witness Highest Growth Globally Coming Years 20192025 February 25, 2020High Purity Alumina Market Survey Report 2019 Along Statistics, Forecasts till 2025 February 25, 2020Ceramic Injection Molding Market Competitive Research And Precise Outlook 2019 To 2025 February 25, 2020Fertility Services Market Survey Report 2019 Along Statistics, Forecasts till 2025 February 25, 2020Varicella Vaccine Market Competitive Research And Precise Outlook 2019 To 2025 February 25, 2020Regulatory Technology RegTech Market Research Technology Outlook 20202026 February 25, 2020Prefabricated Bathroom Pods Market Overview, Scope Advancement Outlook Till 2026 Facebook Twitter WhatsApp Telegram Close for: Close Log In Forget? 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http://spaceref.com/astronomy/observation-simulation-and-ai-join-forces-to-reveal-a-clear-universe.html

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Observation, Simulation, And AI Join Forces To Reveal A Clear Universe - SpaceRef

Observation, Simulation, And AI Join Forces To Reveal A Clear Universe - SpaceRef Home | NASA Watch SpaceRef Business Astrobiology Web Advertising Add an Event Sign up for our Daily Newsletter International Space Station NASA Hack Space Calendar Missions Space Weather   Observation, Simulation, And AI Join Forces To Reveal A Clear Universe Press Release - Source: NATIONAL INSTITUTES OF NATURAL SCIENCES Posted July 4, 2021 10:00 PM View Comments Using AI driven data analysis to peel back the noise and find the actual shape of the Universe. CREDIT The Institute of Statistical Mathematics Japanese astronomers have developed a new artificial intelligence (AI) technique to remove noise in astronomical data due to random variations in galaxy shapes. After extensive training and testing on large mock data created by supercomputer simulations, they then applied this new tool to actual data from Japan's Subaru Telescope and found that the mass distribution derived from using this method is consistent with the currently accepted models of the Universe. This is a powerful new tool for analyzing big data from current and planned astronomy surveys. Wide area survey data can be used to study the large-scale structure of the Universe through measurements of gravitational lensing patterns. In gravitational lensing, the gravity of a foreground object, like a cluster of galaxies, can distort the image of a background object, such as a more distant galaxy. Some examples of gravitational lensing are obvious, such as the "Eye of Horus". The large-scale structure, consisting mostly of mysterious "dark" matter, can distort the shapes of distant galaxies as well, but the expected lensing effect is subtle. Averaging over many galaxies in an area is required to create a map of foreground dark matter distributions. But this technique of looking at many galaxy images runs into a problem; some galaxies are just innately a little funny looking. It is difficult to distinguish between a galaxy image distorted by gravitational lensing and a galaxy that is actually distorted. This is referred to as shape noise and is one of the limiting factors in research studying the large-scale structure of the Universe. To compensate for shape noise, a team of Japanese astronomers first used ATERUI II, the world's most powerful supercomputer dedicated to astronomy, to generate 25,000 mock galaxy catalogs based on real data from the Subaru Telescope. They then added realist noise to these perfectly known artificial data sets, and trained an AI to statistically recover the lensing dark matter from the mock data. After training, the AI was able to recover previously unobservable fine details, helping to improve our understanding of the cosmic dark matter. Then using this AI on real data covering 21 square degrees of the sky, the team found a distribution of foreground mass consistent with the standard cosmological model. "This research shows the benefits of combining different types of research: observations, simulations, and AI data analysis." comments Masato Shirasaki, the leader of the team, "In this era of big data, we need to step across traditional boundaries between specialties and use all available tools to understand the data. If we can do this, it will open new fields in astronomy and other sciences." ### These results appeared as Shirasaki et al. "Noise reduction for weak lensing mass mapping: an application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data" in the June 2021 issue of Monthly Notices of the Royal Astronomical Society. Please follow SpaceRef on Twitter and Like us on Facebook. TAGS: AStronomy FILED UNDER: Astronomy SOURCE: NATIONAL INSTITUTES OF NATURAL SCIENCES Press Release Tweet Observation, Simulation, And AI Join Forces To Reveal A Clear Universe Japanese astronomers have developed a new artificial intelligence (AI) technique to remove noise in astronomical data due to random variations in galaxy shapes. Please enable JavaScript to view the comments powered by Disqus. Follow @SpaceRef   Calendar Events Launches Your Event 14 Jul: The Challenges of Managing Small Space Flight Projects 15 Jul: NASA Aerospace Safety Advisory Panel Meeting 19 Jul: NfoLD/NExSS Standards of Evidence for Life Detection Community Workshop * Submit Your Event  |  More Events * No events for the next 2 days. * Submit Your Event  |  More Launches * Are you hosting an event? We accept all space related events in our calendar and all it takes is about 5 minutes for your to fill out the online event form. Let us help you get the word out about your event. Submit your event today.   Recent Articles Observation, Simulation, And AI Join Forces To Reveal A Clear Universe NASA Weekly ISS Space to Ground Report for 2 July, 2021 NASA Space Station On-Orbit Status 1 July, 2021 - Russian Progress 78 Spacecraft Arrives Earth from Space: North Frisian Islands The Red Sea As Viewed From Space Earth's Cryosphere Is Shrinking By 87,000 Square Kilometers Per Year Plant Water Management Experiment On ISS Closing The Gap On The Missing Lithium NASA Space Station On-Orbit Status 30 June, 2021 - Physics and Biology Studies Cygnus Cargo Droid Departs The ISS Subscribe RSS Twitter Facebook Google+ UStream YouTube Vimeo Newsletter Masthead Tip your [email protected] Senior Editor & Chief Architect:Marc BoucherEmail | Twitter Editor-in-Chief:Keith CowingEmail | Twitter Company Information About SpaceRef Management Contact Information Advertising SpaceRef RSS - XML News Feeds Company Press Releases Employment Copyright Notice Privacy Policy Terms of Use SpaceRef Network SpaceRef NASA Watch SpaceRef Business Astrobiology Web Archives News Archives Press Releases Status Reports Europe Asia Featured Topics NASA Hack Space Hubble Kepler James Webb Telescope Lunar Reconnaissance Orbiter New Horizons Copyright © 2021 SpaceRef Interactive Inc. 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Observation, Simulation, And AI Join Forces To Reveal A Clear Universe

Observation, Simulation, And AI Join Forces To Reveal A Clear Universe SpaceRef Home NASA Watch SpaceRef Business Astrobiology Web Advertising Add Event Sign Daily Newsletter International Space Station NASA Hack Space Calendar Missions Space Weather Observation, Simulation, And AI Join Forces To Reveal A Clear Universe Press Release Source: NATIONAL INSTITUTES OF NATURAL SCIENCES Posted July 4, 2021 10:00 PM View Comments Using AI driven data analysis peel noise actual shape Universe. CREDIT The Institute Statistical Mathematics Japanese astronomers developed new artificial intelligence AI technique remove noise astronomical data random variations galaxy shapes. After extensive training testing large mock data created supercomputer simulations, applied new tool actual data Japans Subaru Telescope mass distribution derived method consistent currently accepted models Universe. This powerful new tool analyzing big data current planned astronomy surveys. Wide area survey data study largescale structure Universe measurements gravitational lensing patterns. In gravitational lensing, gravity foreground object, like cluster galaxies, distort image background object, distant galaxy. Some examples gravitational lensing obvious, Eye Horus. The largescale structure, consisting mysterious dark matter, distort shapes distant galaxies well, expected lensing effect subtle. Averaging galaxies area required create map foreground dark matter distributions. But technique looking galaxy images runs problem; galaxies innately little funny looking. It difficult distinguish galaxy image distorted gravitational lensing galaxy actually distorted. This referred shape noise limiting factors research studying largescale structure Universe. To compensate shape noise, team Japanese astronomers ATERUI II, worlds powerful supercomputer dedicated astronomy, generate 25,000 mock galaxy catalogs based real data Subaru Telescope. They added realist noise perfectly known artificial data sets, trained AI statistically recover lensing dark matter mock data. After training, AI able recover previously unobservable fine details, helping improve understanding cosmic dark matter. Then AI real data covering 21 square degrees sky, team distribution foreground mass consistent standard cosmological model. This research shows benefits combining different types research: observations, simulations, AI data analysis. comments Masato Shirasaki, leader team, In era big data, need step traditional boundaries specialties use available tools understand data. If this, open new fields astronomy sciences. These results appeared Shirasaki et al. Noise reduction weak lensing mass mapping: application generative adversarial networks Subaru Hyper SuprimeCam firstyear data June 2021 issue Monthly Notices Royal Astronomical Society. Please follow SpaceRef Twitter Like Facebook. TAGS: AStronomy FILED UNDER: Astronomy SOURCE: NATIONAL INSTITUTES OF NATURAL SCIENCES Press Release Tweet Observation, Simulation, And AI Join Forces To Reveal A Clear Universe Japanese astronomers developed new artificial intelligence AI technique remove noise astronomical data random variations galaxy shapes. Please enable JavaScript view comments powered Disqus. Follow Calendar Events Launches Your Event 14 Jul: The Challenges Managing Small Space Flight Projects 15 Jul: NASA Aerospace Safety Advisory Panel Meeting 19 Jul: NfoLDNExSS Standards Evidence Life Detection Community Workshop Submit Your Event More Events No events 2 days. Submit Your Event More Launches Are hosting event? We accept space related events calendar takes 5 minutes online event form. Let help word event. Submit event today. Recent Articles Observation, Simulation, And AI Join Forces To Reveal A Clear Universe NASA Weekly ISS Space Ground Report 2 July, 2021 NASA Space Station OnOrbit Status 1 July, 2021 Russian Progress 78 Spacecraft Arrives Earth Space: North Frisian Islands The Red Sea As Viewed From Space Earths Cryosphere Is Shrinking By 87,000 Square Kilometers Per Year Plant Water Management Experiment On ISS Closing The Gap On The Missing Lithium NASA Space Station OnOrbit Status 30 June, 2021 Physics Biology Studies Cygnus Cargo Droid Departs The ISS RSS Twitter Facebook Google UStream YouTube Vimeo Newsletter Masthead Tip editorstips Senior Editor Chief Architect:Marc BoucherEmail Twitter EditorinChief:Keith CowingEmail Twitter Company Information About SpaceRef Management Information Advertising SpaceRef RSS XML News Feeds Company Press Releases Employment Notice Terms Use SpaceRef Network SpaceRef NASA Watch SpaceRef Business Astrobiology Web Archives News Archives Press Releases Status Reports Europe Asia Featured Topics NASA Hack Space Hubble Kepler James Webb Telescope Lunar Reconnaissance Orbiter New Horizons 2021 SpaceRef Interactive . .

http://usweekly.com/news/17/40964/Artificial-intelligence-yields-new-antibiotic.html

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Artificial intelligence yields new antibiotic - USweekly

Artificial intelligence yields new antibiotic - USweekly Sunday, 23 February 2020 Send search form Todays news World U.S. National Politics Business Technology Sports Entertainment Beauty & Health Living & Travel Science Weather Odd news Shopping Artificial intelligence yields new antibiotic Added: 21.02.2020 21:19 | 35 views | 0 comments Source: www.slideshare.netUsing a machine-learning algorithm, researchers have identified a powerful new antibiotic compound. In laboratory tests, the drug killed many of the world's most problematic disease-causing bacteria, including some strains that are resistant to all known antibiotics. It also cleared infections in two different mouse models.More in www.sciencedaily.com » Tags: Mac, Bacteria, Cher Nickname: Enter image code:  Comments: Tags 4K  Audi  Best Buy  Brazil  Breast cancer  Champions League  Cher  Climate change  Congress  Dell  DNA  Dodge  eBay  EU  FBI  FIA  Football  Gamers  GM  Goa  Gold  HP  iOS  Japan  Kimye  Mac  Movies  NATO  NBA  NFL  North Korea  Oil  PC  Players  Premier League  Prison  PS4  Rita Ora  Sex  Social media  SPA  Star Wars  Students  Surgery  Uber  UK  USA  Windows 10  Xbox One  Yahoo  advertising Copyright © 2008 - 2020 USweekly  - all rights reserved Contact us

Artificial intelligence yields new antibiotic

Artificial intelligence yields new antibiotic USweekly Sunday, 23 February 2020 Send search form Todays news World U.S. National Politics Business Technology Sports Entertainment Beauty Health Living Travel Science Weather Odd news Shopping Artificial intelligence yields new antibiotic Added: 21.02.2020 21:19 35 views 0 comments Source: machinelearning algorithm, researchers identified powerful new antibiotic compound. In laboratory tests, drug killed worlds problematic diseasecausing bacteria, including strains resistant known antibiotics. It cleared infections different mouse models.More Tags: Mac, Bacteria, Cher Nickname: Enter image code: Comments: Tags 4K Audi Best Buy Brazil Breast cancer Champions League Cher Climate change Congress Dell DNA Dodge eBay EU FBI FIA Football Gamers GM Goa Gold HP iOS Japan Kimye Mac Movies NATO NBA NFL North Korea Oil PC Players Premier League Prison PS4 Rita Ora Sex Social media SPA Star Wars Students Surgery Uber UK USA Windows 10 Xbox One Yahoo advertising 2008 2020 USweekly

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Forget ML, AI and Industry 4.0 – obsolescence should be your focus - 26 February 2021 - Test & Rework Solutions - Dataweek

Forget ML, AI and Industry 4.0 – obsolescence should be your focus - 26 February 2021 - Test & Rework Solutions - Dataweek Home About us Back issues / E-book / PDF EMP Handbook Subscribe Advertise Categories ▸ Editor's Choice ▸ Multimedia, Videos ▸ Analogue, Mixed Signal, LSI ▸ Circuit & System Protection ▸ Computer/Embedded Technology ▸ Design Automation ▸ DSP, Micros & Memory ▸ Electronics Technology ▸ Enclosures, Racks, Cabinets & Panel Products ▸ Events ▸ Interconnection ▸ Manufacturing / Production Technology, Hardware & Services ▸ News ▸ Opto-Electronics ▸ Passive Components ▸ Power Electronics / Power Management ▸ Programmable Logic ▸ Smart Home Automation ▸ Switches, Relays & Keypads ▸ Telecoms, Datacoms, Wireless, IoT ▸ Test & Measurement Categories Editor's Choice Multimedia, Videos Analogue, Mixed Signal, LSI Circuit & System Protection Computer/Embedded Technology Design Automation DSP, Micros & Memory Electronics Technology Enclosures, Racks, Cabinets & Panel Products Events Interconnection Manufacturing / Production Technology, Hardware & Services News Opto-Electronics Passive Components Power Electronics / Power Management Programmable Logic Smart Home Automation Switches, Relays & Keypads Telecoms, Datacoms, Wireless, IoT Test & Measurement Manufacturing / Production Technology, Hardware & Services Production/manufacturing equipment suppliers & service providers   printer friendly version Forget ML, AI and Industry 4.0 – obsolescence should be your focus 26 February 2021 Manufacturing / Production Technology, Hardware & Services By Willian Santos, international sales manager at ABI Electronics. The world entered a new era of accelerated transformation in the last eighteen months that will continue to evolve and press forward for years to come. Most businesses are playing catch-up trying to make sense of a new timeline where the ten years that had been set aside for careful planning and implementation of what was coming up next no longer exists. The next is happening now and, regardless of your industry or seniority, the status quo has shifted and you better face it. Back in 2019, I was invited to attend a pompous meeting in London at the Brazilian Embassy along with selected leading names from the oil and energy industry, to get an update on what was going to happen in the following decade. I could soon spot all the buzzwords coming up on PowerPoint slides from the different companies presenting: decommissioning, decarbonisation, zero emissions, transition to green energy. Throughout the morning and most of the afternoon, we heard that most of these changes would kick in between 2030-35. And I recall a Shell executive pointing out that the business had explicitly concluded that the current income streams were needed to provide the capital required for the transition to take place in due course.Whomever you talk to in the energy industry in 2021 would tell you to forget about that timeline. Change has come and the transition is happening much faster than anticipated, with big businesses scrambling to find the capital to fund all that is required. I could go on all day talking about how the pandemic and the impact it had on market demand and consumer behaviour messed up the timeline of transport, automotive, defence, manufacturing and so on. For instance, carrier planes that were due to be retired in 2017 are now required to remain in service at least until 2029. Projects in wind power involving upgrades of wind farms have been scrapped and new installations have drawn huge competition from up and coming brands with operators tending to favour smaller turbines – for being efficient and cheaper to run – and choosing to buy from multiple suppliers instead of just one company. Machine learning (ML), AI and Industry 4.0 knock at the door. Still, the biggest change yet to be fully appreciated by business leaders is how electronics effectively became the brain of every industrial asset and all infrastructure equipment in operation in recent years, and therefore what is required to deal with breakdowns and obsolescence. For instance, I lost count of the many rail connections that I have working in operations and maintenance who struggle with their CME – chief mechanical engineer – who, like the role suggests, is usually a mechanically-biased individual who may have a less than ideal approach when it comes to the handling of electronic failures. And you guessed right: less ideal means neither green nor cheap solutions, as illustrated in this video: www.dataweek.co.za/*mar21-abi Many decision-makers only realise the problem at hand and regret not following the tag #RepairDontWaste on LinkedIn in times like this, resulting in comments like, "We did not anticipate that the equipment would become obsolete so quickly!" or, "Spare circuit boards started to take weeks then months to arrive. Then we were told that they would no longer be supplied. We were quoted seven-figure prices to upgrade our assembly line."By deferring the responsibility to the original equipment supplier and other third parties, you are bound to be caught off guard soon or later. And the risk exposure from insisting on this strategy is about to get exponentially higher; there is a widespread component shortage increasing lead times, and technology providers are dropping support for many products in order to remain viable.Last month, a wind turbine multinational in the US had to ship a bunch of expensive controller cards used in inverters and speed governing systems to its workshop in Brazil for repair as new replacement cards are not due back in stock for another six months. Good job that the local team had an electronic diagnostic equipment BoardMaster from ABI in the shop, ready to help the technicians locate the fault and repair the cards, which happened in record time.Poor decision-making when it comes to setting maintenance and repair strategies are often linked to ‘fake news’ and myths such as:• A repaired electronic card (PCB/PCBA) will never be as good as a new one.• The manufacturer told us that special software was required to troubleshoot the card.• Troubleshooting down to the component level is impossible or would take too long.• Dedicated test and repair equipment is prohibitively expensive.The mentality here needs to change, and fast. The longer you take to embrace the new strategies for in-house fault analysis, maintenance, and obsolescence planning of your key electronic circuits and assets, the more expensive it will get to keep them going. Hundreds of leading organisations have found in recent years that the belief in these myths was hindering their results and compromising the operation – from GE Renewable to Collins Aerospace, where the latter reduced troubleshooting times on avionics circuit cards from 50 hours to 10 minutes. Then there is Lego in Mexico, where controller cards from 600+ plastic moulding machines worth tens of thousands of dollars each have been mapped and are now repaired in-house. In the US, an army engineer saved a mobile shelter unit worth 10 million dollars from being decommissioned by repairing its HVAC circuit board that was deemed ‘irreparable’ by the OEM. In the rail arena, companies like Alstom, CAF, Bombardier and operators like Irish Rail, SFMTA, TCDD and so many others have discovered the benefits of investing in the right tool and skill development programme to shield the business against parts shortages and accelerated obsolescence. Some of their stories, and the technology that is driving several top-shelf global sustainability projects, were the subject of a YouTube series called ABI Labs. The series gives an in-depth review of the key tests and techniques available in the British-made BoardMaster hardware and software platform from ABI Electronics to handle predictive as well as corrective maintenance of critical electronics without wasting time or money. The ABI BoardMaster 19-inch rack universal diagnostic system is a uniquely versatile, self-contained and easy-to-use test system. It offers the most comprehensive set of test instruments for fault-finding on almost any kind of PCB. As the product of choice for companies operating in rail transport, aerospace, military, automotive, telecoms and a range of other industrial sectors, the BoardMaster is ABI Electronics’ top of the range solution that saves customers time and money, and increases asset availability and reliability. With the full range of instruments and a variety of test methods guaranteeing the best possible fault coverage, the BoardMaster 19-inch rack provides the ultimate in diagnostic tools.The BoardMaster 19-inch rack comes complete with ABI's multi-licence and user-friendly SYSTEM 8 Ultimate Software preinstalled. Customers are guaranteed to receive free software updates for life and will not be charged for additional seats or maintenance fees. The powerful yet easy-to-use software includes user access management, ABI's exclusive TestFlow Manager, as well as a wealth of customisation options. Credit(s) Test & Rework Solutions Tel: +27 11 704 6677 Email: [email protected] www: www.testandrework.co.za Articles: More information and articles about Test & Rework Solutions Share this article: Further reading: Successful solder processing of high-density connector arrays 26 February 2021, Spectrum Concepts , Manufacturing / Production Technology, Hardware & Services Processing a component onto a printed circuit board (PCB) used to be fairly straightforward. Through-hole products, or a single- or double-row surface mount component with a larger centreline, rarely ... Read more... Water-based cleaners for electronic assemblies 26 February 2021, Vepac Electronics , Manufacturing / Production Technology, Hardware & Services The purpose of cleaning, specifically within the rapidly expanding electronics industry, is essentially to improve product lifetime by ensuring good surface resistance and by preventing current leakage ... Read more... Pathfindr device helps people maintain safe social distancing 26 February 2021, RS Components (SA) , Manufacturing / Production Technology, Hardware & Services The Safe Distancing Assistant from Pathfindr is a new addition to RS Components’ supplier portfolio specialising in asset intelligence using connectivity-enabled location tracking. Designed for use ... Read more... Indium features low-temp alloy at productronica China 26 February 2021, Techmet , Manufacturing / Production Technology, Hardware & Services Indium featured its award-winning Durafuse LT – a novel, low-temperature alloy system designed to provide high reliability in low-temperature applications – during productronica China, which was held ... Read more... Assistance system supports efficient conductor processing 26 February 2021, Phoenix Contact , Manufacturing / Production Technology, Hardware & Services In industrial control cabinet manufacturing, processes still feature multiple manual steps. 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Forget ML, AI and Industry 4.0

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How You Should Validate Machine Learning Models | by Patryk Miziuła, PhD | Jul, 2023 | | #1 NEWS SOURCE FOR PEOPLE OF COLOR ON EARTH !!!!!

How You Should Validate Machine Learning Models | by Patryk Miziuła, PhD | Jul, 2023 | | #1 NEWS SOURCE FOR PEOPLE OF COLOR ON EARTH !!!!! close Home BUSINESS AFRICAN AMERICAN (B) ASIAN (B) HISPANIC (B) INDIAN (B) MIDDLE EASTERN (B) NATIVE AMERICAN (B) ENTERTAINMENT AFRICAN AMERICAN (E) ASIAN (E) HISPANIC (E) INDIAN (E) MIDDLE EASTERN (E) NATIVE AMERICAN (E) HEALTH AFRICAN AMERICAN (H) ASIAN (H) HISPANIC (H INDIAN (H) MIDDLE EASTERN (H) NATIVE AMERICAN (H) POLITICAL AFRICAN AMERICAN (P) ASIAN (P) HISPANIC (P) INDIAN (P) MIDDLE EASTERN (P) NATIVE AMERICAN (P) TECHNOLOGY AFRICAN AMERICAN (T) ASIAN (T) HISPANIC (T) INDIAN (T) MIDDLE EASTERN (T) NATIVE AMERICAN (T) VIDEO NEWS ETHNIC AFRICAN-AMERICANS (ET) ASIANS (ET) HISPANICS (ET) INDIANS (ET) MIDDLE EASTERN (ET) NATIVE AMERICANS(ET) Search for: Recent Posts How You Should Validate Machine Learning Models | by Patryk Miziuła, PhD | Jul, 2023 Cartoon Network and Warner Bros. 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One of the biggest advantages is the fact that for most applications, they can be used as is — we don’t have to train them ourselves. This requires us to reexamine some of the common assumptions about the whole machine learning process — many practitioners consider validation to be “part of the training”, which would suggest that it is no longer needed. We hope that the reader shuddered slightly at the suggestion of validation being obsolete — it most certainly is not. Here, we examine the very idea of model validation and testing. If you believe yourself to be perfectly fluent in the foundations of machine learning, you can skip this article. Otherwise, strap in — we’ve got some far-fetched scenarios for you to suspend your disbelief on. This article is a joint work of Patryk Miziuła, PhD and Jan Kanty Milczek. Imagine that you want to teach someone to recognize the languages of tweets on Twitter. So you take him to a desert island, give him 100 tweets in 10 languages, tell him what language each tweet is in, and leave him alone for a couple of days. After that, you return to the island to check whether he has indeed learned to recognize languages. But how can you examine it? Your first thought may be to ask him about the languages of the tweets he got. So you challenge him this way and he answers correctly for all 100 tweets. Does it really mean he is able to recognize languages in general? Possibly, but maybe he just memorized these 100 tweets! And you have no way of knowing which scenario is true! Here you didn’t check what you wanted to check. Based on such an examination, you simply can’t know whether you can rely on his tweet language recognition skills in a life-or-death situation (those tend to happen when desert islands are involved). What should we do instead? How to make sure he learned, rather than simply memorizing? Give him another 50 tweets and have him tell you their languages! If he gets them right, he is indeed able to recognize the language. But if he fails entirely, you know he simply learned the first 100 tweets off by heart — which wasn’t the point of the whole thing. The story above figuratively describes how machine learning models learn and how we should check their quality: The man in the tale stands for a machine learning model. To disconnect a human from the world you need to take him to a desert island. For a machine learning model it’s easier — it’s just a computer program, so it doesn’t inherently understand the idea of the world. Recognizing the language of a tweet is a classification task, with 10 possible classes, aka categories, as we chose 10 languages. The first 100 tweets used for learning are called the training set. The correct languages attached are called labels. The other 50 tweets only used to examine the man/model are called the test set. Note that we know its labels, but the man/model doesn’t. The graph below shows how to correctly train and test the model: Image 1: scheme for training and testing the model properly. Image by author. So the main rule is: Test a machine learning model on a different piece of data than you trained it on. If the model does well on the training set, but it performs poorly on the test set, we say that the model is overfitted. “Overfitting” means memorizing the training data. That’s definitely not what we want to achieve. Our goal is to have a trained model — good for both the training and the test set. Only this kind of model can be trusted. And only then may we believe that it will perform as well in the final application it’s being built for as it did on the test set. Now let’s take it a step further. Imagine you really really want to teach a man to recognize the languages of tweets on Twitter. So you find 1000 candidates, take each to a different desert island, give each the same 100 tweets in 10 languages, tell each what language each tweet is in and leave them all alone for a couple of days. After that, you examine each candidate with the same set of 50 different tweets. Which candidate will you choose? Of course, the one who did the best on the 50 tweets. But how good is he really? Can we truly believe that he’s going to perform as well in the final application as he did on these 50 tweets? The answer is no! Why not? To put it simply, if every candidate knows some answers and guesses some of the others, then you choose the one who got the most answers right, not the one who knew the most. He is indeed the best candidate, but his result is inflated by “lucky guesses.” It was likely a big part of the reason why he was chosen. To show this phenomenon in numerical form, imagine that 47 tweets were easy for all the candidates, but the 3 remaining messages were so hard for all the competitors that they all simply guessed the languages blindly. Probability says that the chance that somebody (possibly more than one person) got all the 3 hard tweets is above 63% (info for math nerds: it’s almost 1–1/e). So you’ll probably choose someone who scored perfectly, but in fact he’s not perfect for what you need. Perhaps 3 out of 50 tweets in our example don’t sound astonishing, but for many real-life cases this discrepancy tends to be much more pronounced. So how can we check how good the winner actually is? Yes, we have to procure yet another set of 50 tweets, and examine him once again! Only this way will we get a score we can trust. This level of accuracy is what we expect from the final application. In terms of names: The first set of 100 tweets is now still the training set, as we use it to train the models. But now the purpose of the second set of 50 tweets has changed. This time it was used to compare different models. Such a set is called the validation set. We already understand that the result of the best model examined on the validation set is artificially boosted. This is why we need one more set of 50 tweets to play the role of the test set and give us reliable information about the quality of the best model. You can find the flow of using the training, validation and test set in the image below: Image 2: scheme for training, validating and testing the models properly. Image by author. Here are the two general ideas behind these numbers: Put as much data as possible into the training set. The more training data we have, the broader the look the models take and the greater the chance of training instead of overfitting. The only limits should be data availability and the costs of processing the data. Put as small an amount of data as possible into the validation and test sets, but make sure they’re big enough. Why? Because you don’t want to waste much data for anything but training. But on the other hand you probably feel that evaluating the model based on a single tweet would be risky. So you need a set of tweets big enough not to be afraid of score disruption in case of a small number of really weird tweets. And how to convert these two guidelines into exact numbers? If you have 200 tweets available then the 100/50/50 split seems fine as it obeys both the rules above. But if you’ve got 1,000,000 tweets then you can easily go into 800,000/100,000/100,000 or even 900,000/50,000/50,000. Maybe you saw some percentage clues somewhere, like 60%/20%/20% or so. Well, they are only an oversimplification of the two main rules written above, so it’s better to simply stick to the original guidelines. We believe this main rule appears clear to you at this point: Use three different pieces of data for training, validating, and testing the models. So what if this rule is broken? What if the same or almost the same data, whether by accident or a failure to pay attention, go into more than one of the three datasets? This is what we call data leakage. The validation and test sets are no longer trustworthy. We can’t tell whether the model is trained or overfitted. We simply can’t trust the model. Not good. Perhaps you think these problems don’t concern our desert island story. We just take 100 tweets for training, another 50 for validating and yet another 50 for testing and that’s it. Unfortunately, it’s not so simple. We have to be very careful. Let’s go through some examples. Assume that you scraped 1,000,000 completely random tweets from Twitter. Different authors, time, topics, localizations, numbers of reactions, etc. Just random. And they are in 10 languages and you want to use them to teach the model to recognize the language. Then you don’t have to worry about anything and you can simply draw 900,000 tweets for the training set, 50,000 for the validation set and 50,000 for the test set. This is called the random split. Why draw at random, and not put the first 900,000 tweets in the training set, the next 50,000 in the validation set and the last 50,000 in the test set? Because the tweets can initially be sorted in a way that wouldn’t help, such as alphabetically or by the number of characters. And we have no interest in only putting tweets starting with ‘Z’ or the longest ones in the test set, right? So it’s just safer to draw them randomly. Image 3: random data split. Image by author. The assumption that the tweets are completely random is strong. Always think twice if that’s true. In the next examples you’ll see what happens if it’s not. If we only have 200 completely random tweets in 10 languages then we can still split them randomly. But then a new risk arises. Suppose that a language is predominant with 128 tweets and there are 8 tweets for each of the other 9 languages. Probability says that then the chance that not all the languages will go to the 50-element test set is above 61% (info for math nerds: use the inclusion-exclusion principle). But we definitely want to test the model on all 10 languages, so we definitely need all of them in the test set. What should we do? We can draw tweets class-by-class. So take the predominant class of 128 tweets, draw the 64 tweets for the training set, 32 for the validation set and 32 for the test set. Then do the same for all the other classes — draw 4, 2 and 2 tweets for training, validating and testing for each class respectively. This way, you’ll form three sets of the sizes you need, each with all classes in the same proportions. This strategy is called the stratified random split. The stratified random split seems better/safer than the ordinary random split, so why didn’t we use it in Example 1? Because we didn’t have to! What often defies intuition is that if 5% out of 1,000,000 tweets are in English and we draw 50,000 tweets with no regard for language, then 5% of the tweets drawn will also be in English. This is how probability works. But probability needs big enough numbers to work properly, so if you have 1,000,000 tweets then you don’t care, but if you only have 200, watch out. Now assume that we’ve got 100,000 tweets, but they are from only 20 institutions (let’s say a news TV station, a big soccer club, etc.), and each of them runs 10 Twitter accounts in 10 languages. And again our goal is to recognize the Twitter language in general. Can we simply use the random split? You’re right — if we could, we wouldn’t have asked. But why not? To understand this, first let’s consider an even simpler case: what if we trained, validated and tested a model on tweets from one institution only? Could we use this model on any other institution’s tweets? We don’t know! Maybe the model would overfit the unique tweeting style of this institution. We wouldn’t have any tools to check it! Let’s return to our case. The point is the same. The total number of 20 institutions is on the small side. So if we use data from the same 20 institutions to train, compare and score the models, then maybe the model overfits the 20 unique styles of these 20 institutions and will fail on any other author. And again there is no way to check it. Not good. So what to do? Let’s follow one more main rule: Validation and test sets should simulate the real case which the model will be applied to as faithfully as possible. Now the situation is clearer. Since we expect different authors in the final application than we have in our data, we should also have different authors in the validation and test sets than we have in the training set! And the way to do so is to split data by institutions! If we draw, for example, 10 institutions for the training set, another 5 for the validation set and put the last 5 in the test set, the problem is solved. Image 4: stratified data split. Image by author. Note that any less strict split by institution (like putting the whole of 4 institutions and a small part of the 16 remaining ones in the test set) would be a data leak, which is bad, so we have to be uncompromising when it comes to separating the institutions. A sad final note: for a correct validation split by institution, we may trust our solution for tweets from different institutions. But tweets from private accounts may — and do — look different, so we can’t be sure the model we have will perform well for them. With the data we have, we have no tool to check it… Example 3 is hard, but if you went through it carefully then this one will be fairly easy. So, assume that we have exactly the same data as in Example 3, but now the goal is different. This time we want to recognize the language of other tweets from the same 20 institutions that we have in our data. Will the random split be OK now? The answer is: yes. The random split perfectly follows the last main rule above as we are ultimately only interested in the institutions we have in our data. Examples 3 and 4 show us that the way we should split the data does not depend only on the data we have. It depends on both the data and the task. Please bear that in mind whenever you design the training/validation/test split. In the last example let’s keep the data we have, but now let’s try to teach a model to predict the institution from future tweets. So we once again have a classification task, but this time with 20 classes as we’ve got tweets from 20 institutions. What about this case? Can we split our data randomly? As before, let’s think about a simpler case for a while. Suppose we only have two institutions — a TV news station and a big soccer club. What do they tweet about? Both like to jump from one hot topic to another. Three days about Trump or Messi, then three days about Biden and Ronaldo, and so on. Clearly, in their tweets we can find keywords that change every couple of days. And what keywords will we see in a month? Which politician or villain or soccer player or soccer coach will be ‘hot’ then? Possibly one that is completely unknown right now. So if you want to learn to recognize the institution, you shouldn’t focus on temporary keywords, but rather try to catch the general style. OK, let’s move back to our 20 institutions. The above observation remains valid: the topics of tweets change over time, so as we want our solution to work for future tweets, we shouldn’t focus on short-lived keywords. But a machine learning model is lazy. If it finds an easy way to fulfill the task, it doesn’t look any further. And sticking to keywords is just such an easy way. So how can we check whether the model learned properly or just memorized the temporary keywords? We’re pretty sure you realize that if you use the random split, you should expect tweets about every hero-of-the-week in all the three sets. So this way, you end up with the same keywords in the training, validation and test sets. This is not what we’d like to have. We need to split smarter. But how? When we go back to the last main rule, it becomes easy. We want to use our solution in future, so validation and test sets should be the future with respect to the training set! We should split data by time. So if we have, say, 12 months of data — from July 2022 up to June 2023 — then putting July 2022 — April 2023 in the test set, May 2023 in the validation set and June 2023 in the test set should do the job. Image 5: data split by time. Image by author. Maybe you are concerned that with the split by time we don’t check the model’s quality throughout the seasons. You’re right, that’s a problem. But still a smaller problem than we’d get if we split randomly. You can also consider, for example, the following split: 1st-20th of every month to the training set, 20th-25th of every month to the validation set, 25th-last of every month to the test set. In any case, choosing a validation strategy is a trade-off between potential data leaks. As long as you understand it and consciously choose the safest option, you’re doing well. We set

How You Should Validate Machine Learning Models

How You Should Validate Machine Learning Models Patryk Miziua, PhD Jul, 2023 1 NEWS SOURCE FOR PEOPLE OF COLOR ON EARTH close Home BUSINESS AFRICAN AMERICAN B ASIAN B HISPANIC B INDIAN B MIDDLE EASTERN B NATIVE AMERICAN B ENTERTAINMENT AFRICAN AMERICAN E ASIAN E HISPANIC E INDIAN E MIDDLE EASTERN E NATIVE AMERICAN E HEALTH AFRICAN AMERICAN H ASIAN H HISPANIC H INDIAN H MIDDLE EASTERN H NATIVE AMERICAN H POLITICAL AFRICAN AMERICAN P ASIAN P HISPANIC P INDIAN P MIDDLE EASTERN P NATIVE AMERICAN P TECHNOLOGY AFRICAN AMERICAN T ASIAN T HISPANIC T INDIAN T MIDDLE EASTERN T NATIVE AMERICAN T VIDEO NEWS ETHNIC AFRICANAMERICANS ET ASIANS ET HISPANICS ET INDIANS ET MIDDLE EASTERN ET NATIVE AMERICANSET for: Recent Posts How You Should Validate Machine Learning Models Patryk Miziua, PhD Jul, 2023 Cartoon Network Warner Bros. 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One biggest advantages fact applications, dont train ourselves. This requires reexamine common assumptions machine learning process practitioners consider validation training, suggest longer needed. We hope reader shuddered slightly suggestion validation obsolete certainly not. Here, examine idea model validation testing. If believe perfectly fluent foundations machine learning, skip article. Otherwise, strap weve got farfetched scenarios suspend disbelief on. This article joint work Patryk Miziua, PhD Jan Kanty Milczek. Imagine want teach recognize languages tweets Twitter. So desert island, 100 tweets 10 languages, tell language tweet in, leave couple days. After that, return island check learned recognize languages. But examine it? Your thought ask languages tweets got. So challenge way answers correctly 100 tweets. Does mean able recognize languages general? Possibly, maybe memorized 100 tweets And way knowing scenario true Here didnt check wanted check. Based examination, simply know rely tweet language recognition skills lifeordeath situation tend happen desert islands involved. What instead? How sure learned, simply memorizing? Give 50 tweets tell languages If gets right, able recognize language. But fails entirely, know simply learned 100 tweets heart wasnt point thing. The story figuratively describes machine learning models learn check quality: The man tale stands machine learning model. To disconnect human world need desert island. For machine learning model easier program, doesnt inherently understand idea world. Recognizing language tweet classification task, 10 possible classes, aka categories, chose 10 languages. The 100 tweets learning called training set. The correct languages attached called labels. The 50 tweets examine manmodel called test set. Note know labels, manmodel doesnt. The graph shows correctly train test model: Image 1: scheme training testing model properly. Image author. So main rule is: Test machine learning model different piece data trained on. If model training set, performs poorly test set, model overfitted. Overfitting means memorizing training data. Thats definitely want achieve. Our goal trained model good training test set. Only kind model trusted. And believe perform final application built test set. Now lets step further. Imagine want teach man recognize languages tweets Twitter. So 1000 candidates, different desert island, 100 tweets 10 languages, tell language tweet leave couple days. After that, examine candidate set 50 different tweets. Which candidate choose? Of course, best 50 tweets. But good really? Can truly believe hes going perform final application 50 tweets? The answer Why not? To simply, candidate knows answers guesses others, choose got answers right, knew most. He best candidate, result inflated lucky guesses. It likely big reason chosen. To phenomenon numerical form, imagine 47 tweets easy candidates, 3 remaining messages hard competitors simply guessed languages blindly. Probability says chance somebody possibly person got 3 hard tweets 63 info math nerds: 11e. So youll probably choose scored perfectly, fact hes perfect need. Perhaps 3 50 tweets example dont sound astonishing, reallife cases discrepancy tends pronounced. So check good winner actually is? Yes, procure set 50 tweets, examine Only way score trust. This level accuracy expect final application. In terms names: The set 100 tweets training set, use train models. But purpose second set 50 tweets changed. This time compare different models. Such set called validation set. We understand result best model examined validation set artificially boosted. This need set 50 tweets play role test set reliable information quality best model. You flow training, validation test set image below: Image 2: scheme training, validating testing models properly. Image author. Here general ideas numbers: Put data possible training set. The training data have, broader look models greater chance training instead overfitting. The limits data availability costs processing data. Put small data possible validation test sets, sure theyre big enough. Why? Because dont want waste data training. But hand probably feel evaluating model based single tweet risky. So need set tweets big afraid score disruption case small number weird tweets. And convert guidelines exact numbers? If 200 tweets available 1005050 split fine obeys rules above. But youve got 1,000,000 tweets easily 800,000100,000100,000 900,00050,00050,000. Maybe saw percentage clues somewhere, like 602020 so. Well, oversimplification main rules written above, better simply stick original guidelines. We believe main rule appears clear point: Use different pieces data training, validating, testing models. So rule broken? What data, accident failure pay attention, datasets? This data leakage. The validation test sets longer trustworthy. We tell model trained overfitted. We simply trust model. Not good. Perhaps think problems dont concern desert island story. We 100 tweets training, 50 validating 50 testing thats it. Unfortunately, simple. We careful. Lets examples. Assume scraped 1,000,000 completely random tweets Twitter. Different authors, time, topics, localizations, numbers reactions, etc. Just random. And 10 languages want use teach model recognize language. Then dont worry simply draw 900,000 tweets training set, 50,000 validation set 50,000 test set. This called random split. Why draw random, 900,000 tweets training set, 50,000 validation set 50,000 test set? Because tweets initially sorted way wouldnt help, alphabetically number characters. And putting tweets starting Z longest ones test set, right? So safer draw randomly. Image 3: random data split. Image author. The assumption tweets completely random strong. Always think twice thats true. In examples youll happens not. If 200 completely random tweets 10 languages split randomly. But new risk arises. Suppose language predominant 128 tweets 8 tweets 9 languages. Probability says chance languages 50element test set 61 info math nerds: use inclusionexclusion principle. But definitely want test model 10 languages, definitely need test set. What do? We draw tweets classbyclass. So predominant class 128 tweets, draw 64 tweets training set, 32 validation set 32 test set. Then classes draw 4, 2 2 tweets training, validating testing class respectively. This way, youll form sets sizes need, classes proportions. This strategy called stratified random split. The stratified random split bettersafer ordinary random split, didnt use Example 1? Because didnt What defies intuition 5 1,000,000 tweets English draw 50,000 tweets regard language, 5 tweets drawn English. This probability works. But probability needs big numbers work properly, 1,000,000 tweets dont care, 200, watch out. Now assume weve got 100,000 tweets, 20 institutions lets news TV station, big soccer club, etc., runs 10 Twitter accounts 10 languages. And goal recognize Twitter language general. Can simply use random split? Youre right could, wouldnt asked. But not? To understand this, lets consider simpler case: trained, validated tested model tweets institution only? Could use model institutions tweets? We dont know Maybe model overfit unique tweeting style institution. We wouldnt tools check Lets return case. The point same. The total number 20 institutions small side. So use data 20 institutions train, compare score models, maybe model overfits 20 unique styles 20 institutions fail author. And way check it. Not good. So do? Lets follow main rule: Validation test sets simulate real case model applied faithfully possible. Now situation clearer. Since expect different authors final application data, different authors validation test sets training set And way split data institutions If draw, example, 10 institutions training set, 5 validation set 5 test set, problem solved. Image 4: stratified data split. Image author. Note strict split institution like putting 4 institutions small 16 remaining ones test set data leak, bad, uncompromising comes separating institutions. A sad final note: correct validation split institution, trust solution tweets different institutions. But tweets private accounts look different, sure model perform them. With data have, tool check Example 3 hard, went carefully fairly easy. So, assume exactly data Example 3, goal different. This time want recognize language tweets 20 institutions data. Will random split OK now? The answer is: yes. The random split perfectly follows main rule ultimately interested institutions data. Examples 3 4 way split data depend data have. It depends data task. Please bear mind design trainingvalidationtest split. In example lets data have, lets try teach model predict institution future tweets. So classification task, time 20 classes weve got tweets 20 institutions. What case? Can split data randomly? As before, lets think simpler case while. Suppose institutions TV news station big soccer club. What tweet about? Both like jump hot topic another. Three days Trump Messi, days Biden Ronaldo, on. Clearly, tweets keywords change couple days. And keywords month? Which politician villain soccer player soccer coach hot then? Possibly completely unknown right now. So want learn recognize institution, shouldnt focus temporary keywords, try catch general style. OK, lets 20 institutions. The observation remains valid: topics tweets change time, want solution work future tweets, shouldnt focus shortlived keywords. But machine learning model lazy. If finds easy way fulfill task, doesnt look further. And sticking keywords easy way. So check model learned properly memorized temporary keywords? Were pretty sure realize use random split, expect tweets herooftheweek sets. So way, end keywords training, validation test sets. This wed like have. We need split smarter. But how? When main rule, easy. We want use solution future, validation test sets future respect training set We split data time. So have, say, 12 months data July 2022 June 2023 putting July 2022 April 2023 test set, May 2023 validation set June 2023 test set job. Image 5: data split time. Image author. Maybe concerned split time dont check models quality seasons. Youre right, thats problem. But smaller problem wed split randomly. You consider, example, following split: 1st20th month training set, 20th25th month validation set, 25thlast month test set. In case, choosing validation strategy tradeoff potential data leaks. As long understand consciously choose safest option, youre well. We set

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Applitools Visual AI Reaches One Billion Images Analyzed | | IT Business Net

Applitools Visual AI Reaches One Billion Images Analyzed | | IT Business Net Skip to content IT Business Net News for IT Professionals Primary Menu IT Business Net Search for: Home2020JuneApplitools Visual AI Reaches One Billion Images Analyzed News Applitools Visual AI Reaches One Billion Images Analyzed SAN MATEO, Calif., June 23, 2020 /PRNewswire/ — Applitools (https://applitools.com/), provider of a next generation test automation platform through Visual AI and Ultrafast Grid, announced its Visual AI technology has surpassed one billion images analyzed since launching commercially and is projected to double year over year. Each of the one billion images analyzed includes AI enhancing data from over 130 different browser and version combinations, viewport information, and direct user feedback on defects, dynamic content, and more. This uniquely enables Applitools to maintain 99.9999% accuracy to hundreds of Enterprise customers worldwide including the top 10 software companies worldwide, five out of the 10 top banks in the US, and many other Global 500 companies. Applitools Visual AI is rapidly outpacing the competition to visually validate business applications now forced into rapid digital transformation by the COVID-19 pandemic. Only Visual AI is agile enough to handle the complex and evolving challenges organizations face delivering quality modern applications including: Cross-browser, cross-device, and cross-platform testing Dynamic content Responsive design for multiple screen sizes Validation of compliance and regulatory requirements Continuous Integration and Deployment (CI/CD) without compromising quality End-to-end testing, integration testing, unit testing, and component level testing Support of joint development and test engineering teams (DevOps and Shift Left) Pre-production testing and post-production monitoring automation and maintenance “With Applitools, we’ve been able to make visual validation a first-class citizen within our CI/CD ecosystem,” said Mike Millgate, Technical Quality Architect at Gannett. “Our CI environment executes tens of thousands of Visual AI-powered tests against the grid each month. Since implementing it, we’ve been able to remove frail functional tests from our ecosystem and achieve a 99.8% pass percentage. We are faster, more stable, and ship with confidence with Applitools Visual AI running on the Ultrafast Grid.” Recent comparisons show that Applitools Visual AI outperforms any other UI validation technology including those leveraging pixel-to-pixel and DOM based analysis approaches. Applitools Visual AI saves customers from time consuming and disruptive false positives that often break builds and block the continuous integration and delivery (CI/CD) so vital to modern dev team success. Applitools customers also benefit from automated maintenance AI algorithms that significantly speed up the test results analysis process by automating repetitive maintenance operations. For example, automated maintenance AI allows users to approve or reject a bug only once on a single browser, single device, and in a single screen size. Then the AI engine automatically extends that operation across all devices, browsers, form factors and applications pages. This additional level of AI can also group multiple test results that reflect the same bug to allow the user to review only a single occurrence of each bug without spending time reviewing tens, hundreds, and sometimes thousands of repetitions of the same bug on multiple environments. “This is the perfect example of how AI should be applied to solve complex, real world problems,” said Greg Sypolt from EverFi. “By eliminating the false positives associated with pixel and DOM based comparison tools, our team saves huge amounts of time and money. We simply can’t tolerate wasted time if we want to deliver quality applications as quickly as business teams need them in today’s global environment. Visual AI is a must for quality management of modern apps.” To fully understand the differences between Visual AI and pixel-based tools for visual validation, read this recent InfoQ article (https://www.infoq.com/articles/visual-ai-web-app-testing/).  Developers using modern development frameworks like Storybook, or developer friendly testing frameworks like Cypress, Espreso, XCUITest, etc. to test early and often in the software development cycle also see huge value in Visual AI. “I love how Applitools Eyes has the smarts to ignore minor visual differences in your components, especially when testing on different browser types where it’s easy for the layout to be off by a pixel,” said Kent C. Dodds, Google Developer Expert and Creator of TestingJavaScript.com. “The largest brands in the world trust Applitools because our Visual AI technology accurately emulates the human eye and brain to compare screenshot images, not pixels, for visual differences,” said Adam Carmi CTO and Co-Founder of Applitools. “Over the years we have collected data from thousands of applications and web sites that are fully tagged and indexed in a way that shows complete user journeys inside customer’s applications, bugs vs valid application changes, dynamic page regions, and more. To be able to see the same state of an app across different browsers, operating systems, viewport sizes, and device types over an extended period of time and app versions for such a large variety of apps is priceless.” Try the world’s most intelligent test automation platform for free at (https://auth.applitools.com/users/register) or talk to one of our Visual AI experts directly. To learn more about Applitools and the latest developments around Visual AI, visit (https://applitools.com/). About Applitools Applitools delivers a Next Generation Test Automation Platform through Visual AI and Ultrafast Grid. We enable engineering teams to release high quality web and mobile apps at incredible speed and at a reduced cost. Applitools Visual AI modernizes important test automation use cases — Functional Testing, Visual Testing, Web and Mobile UI/UX Testing, Cross Browser Testing, Responsive Web Design Testing, Cross Device Testing, PDF Testing, Accessibility Testing and Compliance Testing — to transform the way organizations deliver innovation at the speed of CI/CD at a significantly lower Total Cost of Ownership (TCO). Hundreds of companies from verticals such as Tech, Banking, Insurance, Retail, Pharma, and Publishing — including 50 of the Fortune 100 — use Applitools to deliver the best possible digital experiences to millions of customers on any device and browser, and across every screen size and operating system. Applitools is headquartered in San Mateo, California, with an R&D center in Tel Aviv, Israel. For more information, please visit applitools.com. Media Contact:Jeremy DouglasCatapult PR-IR+1 303-581-7760, ext. 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Applitools Visual AI Reaches One Billion Images Analyzed

Applitools Visual AI Reaches One Billion Images Analyzed IT Business Net Skip content IT Business Net News IT Professionals Primary Menu IT Business Net for: Home2020JuneApplitools Visual AI Reaches One Billion Images Analyzed News Applitools Visual AI Reaches One Billion Images Analyzed SAN MATEO, Calif., June 23, 2020 PRNewswire Applitools provider generation test automation platform Visual AI Ultrafast Grid, announced Visual AI technology surpassed billion images analyzed launching commercially projected double year year. Each billion images analyzed includes AI enhancing data 130 different browser version combinations, viewport information, direct user feedback defects, dynamic content, more. This uniquely enables Applitools maintain 99.9999 accuracy hundreds Enterprise customers worldwide including 10 software companies worldwide, 10 banks US, Global 500 companies. Applitools Visual AI rapidly outpacing competition visually validate business applications forced rapid digital transformation COVID19 pandemic. Only Visual AI agile handle complex evolving challenges organizations face delivering quality modern applications including: Crossbrowser, crossdevice, crossplatform testing Dynamic content Responsive design multiple screen sizes Validation compliance regulatory requirements Continuous Integration Deployment CICD compromising quality Endtoend testing, integration testing, unit testing, component level testing Support joint development test engineering teams DevOps Shift Left Preproduction testing postproduction monitoring automation maintenance With Applitools, weve able visual validation firstclass citizen CICD ecosystem, said Mike Millgate, Technical Quality Architect Gannett. Our CI environment executes tens thousands Visual AIpowered tests grid month. Since implementing it, weve able remove frail functional tests ecosystem achieve 99.8 pass percentage. We faster, stable, ship confidence Applitools Visual AI running Ultrafast Grid. Recent comparisons Applitools Visual AI outperforms UI validation technology including leveraging pixeltopixel DOM based analysis approaches. Applitools Visual AI saves customers time consuming disruptive false positives break builds block continuous integration delivery CICD vital modern dev team success. Applitools customers benefit automated maintenance AI algorithms significantly speed test results analysis process automating repetitive maintenance operations. For example, automated maintenance AI allows users approve reject bug single browser, single device, single screen size. Then AI engine automatically extends operation devices, browsers, form factors applications s. This additional level AI group multiple test results reflect bug allow user review single occurrence bug spending time reviewing tens, hundreds, thousands repetitions bug multiple environments. This perfect example AI applied solve complex, real world problems, said Greg Sypolt EverFi. By eliminating false positives associated pixel DOM based comparison tools, team saves huge amounts time money. We simply tolerate wasted time want deliver quality applications quickly business teams need todays global environment. Visual AI quality management modern apps. To fully understand differences Visual AI pixelbased tools visual validation,read recent InfoQ article Developers modern development frameworks like Storybook, developer friendly testing frameworks like Cypress, Espreso, XCUITest, etc. test early software development cycle huge value Visual AI. 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http://www.sbwire.com/press-releases/machine-learning-data-catalog-software-market-2020-industry-trends-growth-insight-market-share-competitive-outlook-regional-and-global-industry-forecast-to-2028-1280539.htm

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Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends and

Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends and SBWire Sign Up Login Our Service Plans & Pricing Newsroom Help About Stratagem Market Insights Email Alerts| RSS Machine Learning Data Catalog Software Market 2020 Industry Trends, Growth Insight, Market Share, Competitive Outlook, Regional, and Global Industry Forecast to 2028 Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends and Forecast Research upto 2027   Seattle, WA -- (SBWIRE) -- 03/06/2020 -- A crucial report added by Stratagem Market Insights titled Machine Learning Data Catalog Software Market 2020: Industry Analysis, Geographical Segmentation, Competitive Landscape by Top Key Players and Forecast 2027 provides a comprehensive analysis of the developments, growth outlook, driving factors, and key players of the Machine Learning Data Catalog Software market. The research study concisely divides the Machine Learning Data Catalog Software and determine valuable estimations related to the profit projections, market size, sales capacity, and numerous other crucial parameters. Also, the Machine Learning Data Catalog Software Market report appraises the industry fragments as well as the driving factors impacting the remuneration scale of this industry. The report offers a systematic presentation of the existing trends, growth opportunities, market dynamics that are expected to shape the growth of the Machine Learning Data Catalog Software market. The various research methods and tools were involved in the market analysis of Machine Learning Data Catalog Software, to uncover crucial information about the market such as current & future trends, opportunities, business strategies and more, which in turn will aid the business decision-makers to take a right decision in future. The outcome of our research analysis examines that the Machine Learning Data Catalog Software Market is destined to perceive constant growth in the coming years. For Better Understanding – Go With This Free Sample Report Enabled With Respective Tables and Figures: https://www.stratagemmarketinsights.com/sample/3274 (Our FREE SAMPLE COPY of the report gives a brief introduction to the research report outlook, TOC, list of tables and figures, an outlook to key players of the market and comprising key regions.) Major Company Profiles Covered in This Report: (IBM, Alation, Oracle, Cloudera, Unifi, Anzo Smart Data Lake (ASDL), Collibra, Informatica, Hortonworks, Reltio, Talend) Market Segmentation: The Machine Learning Data Catalog Software Market has been segregated into various crucial divisions including applications, types, and regions. Each market segment is intensively studied in the report contemplating its market acceptance, worthiness, demand, and growth prospects. The segmentation analysis will help the client to customize their marketing approach to have a better command of each segment and to identify the most prospective customer base. Regional Insights of Machine Learning Data Catalog Software Market 1. Asia-Pacific has recorded impressive growth in Machine Learning Data Catalog Software Industry, both in volume and Machine Learning Data Catalog Software and is expected to highest growth rate during the forecast period owing to increasing adoption of automation by manufacturing industries and adoption of industrial Machine Learning Data Catalog Software throughout the region. 2. Countries such as China, Japan, Thailand, and South Korea are manufacturing both commercial and industrial Machine Learning Data Catalog Software in high volume. The adoption rate of Machine Learning Data Catalog Software in China and India is very high, owing to the massive deployment in the manufacturing sector. 3. For instance, The National Authorities are planning to make the amendments in-laws to boost the economy with the change in the latest trends and recently tied up with other worldwide nations on it as well. 4. The Machine Learning Data Catalog Software market research report outlines the Regional key trends, market sizing and forecasting for various emerging sub-segments of the market. Ask For Exclusive Discount: https://www.stratagemmarketinsights.com/discount/3274 Different sales strategies have been elaborated in the report to get a clear idea for getting global clients rapidly. It helps various industry experts, policymakers, business owners as well as various c level people to make informed decisions in the businesses. It includes the massive data relating to the technological advancements, trending products or services observed in the market. The major key pillars of businesses are explained in a concise manner and effectively for fueling the progress of the market. Our Study Report Offers: Market share analysis for the regional and country-level segments. Machine Learning Data Catalog Software Market share analysis of the best business players. Strategic proposal for the new entrants. Market forecasts for the next five years of all the mentioned segments, sub-segments and conjointly the regional markets. Market Opportunities, Trends, Constraints, Threats, Challenges, Drivers, Investment and suggestions. Strategic steerage in key business segments supported the market estimations. Competitive landscaping mapping the key common trends. Company identification with careful methods, financials, and up so far developments. provide chain trends mapping the foremost recent technological advancements. The report's conclusion reveals the overall scope of the Global Machine Learning Data Catalog Software Market in terms of feasibility of investments in the various segments of the market, along with a descriptive passage that outlines the feasibility of new projects that might succeed in the market in the near future. If you have any special requirement please let us know we will offer you a report as you want. Talk to Our Analyst for any Special Requirement/Customization of the report: https://www.stratagemmarketinsights.com/speakanalyst/3274 Media Relations Contact Mr. Shah CEOStratagem Market Insights1-415-871-0703 Email | Web | Profile Follow Stratagem Market Insights   Stratagem Market Insights - Logo   Site Preview: Visit Full Site Close Preview   Source: Stratagem Market Insights Posted Friday, March 06, 2020 at 10:49 AM CST - Permalink   For information regarding the content of this press release please contact the media relations contact listed above directly. 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Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends and

Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends SBWire Sign Up Our Service Plans Pricing Newsroom Help About Stratagem Market Insights Email Alerts RSS Machine Learning Data Catalog Software Market 2020 Industry Trends, Growth Insight, Market Share, Competitive Outlook, Regional, Global Industry Forecast 2028 Machine Learning Data Catalog Software Market 2020: Insights, Key Strategies, Innovative Trends Forecast Research upto 2027 Seattle, WA SBWIRE 03062020 A crucial report added Stratagem Market Insights titled Machine Learning Data Catalog Software Market 2020: Industry Analysis, Geographical Segmentation, Competitive Landscape Top Key Players Forecast 2027 provides comprehensive analysis developments, growth outlook, driving factors, key players Machine Learning Data Catalog Software market. The research study concisely divides Machine Learning Data Catalog Software determine valuable estimations related profit projections, market size, sales capacity, numerous crucial parameters. Also, Machine Learning Data Catalog Software Market report appraises industry fragments driving factors impacting remuneration scale industry. The report offers systematic presentation existing trends, growth opportunities, market dynamics expected shape growth Machine Learning Data Catalog Software market. The research methods tools involved market analysis Machine Learning Data Catalog Software, uncover crucial information market current future trends, opportunities, business strategies more, turn aid business decisionmakers right decision future. The outcome research analysis examines Machine Learning Data Catalog Software Market destined perceive constant growth coming years. For Better Understanding Go With This Free Sample Report Enabled With Respective Tables Figures: Our FREE SAMPLE COPY report gives brief introduction research report outlook, TOC, list tables figures, outlook key players market comprising key regions. Major Company Profiles Covered This Report: IBM, Alation, Oracle, Cloudera, Unifi, Anzo Smart Data Lake ASDL, Collibra, Informatica, Hortonworks, Reltio, Talend Market Segmentation: The Machine Learning Data Catalog Software Market segregated crucial divisions including applications, types, regions. Each market segment intensively studied report contemplating market acceptance, worthiness, demand, growth prospects. The segmentation analysis help client customize marketing approach better command segment identify prospective customer base. Regional Insights Machine Learning Data Catalog Software Market 1. AsiaPacific recorded impressive growth Machine Learning Data Catalog Software Industry, volume Machine Learning Data Catalog Software expected highest growth rate forecast period owing increasing adoption automation manufacturing industries adoption industrial Machine Learning Data Catalog Software region. 2. Countries China, Japan, Thailand, South Korea manufacturing commercial industrial Machine Learning Data Catalog Software high volume. The adoption rate Machine Learning Data Catalog Software China India high, owing massive deployment manufacturing sector. 3. For instance, The National Authorities planning amendments inlaws boost economy change latest trends recently tied worldwide nations well. 4. The Machine Learning Data Catalog Software market research report outlines Regional key trends, market sizing forecasting emerging subsegments market. Ask For Exclusive Discount: Different sales strategies elaborated report clear idea getting global clients rapidly. It helps industry experts, policymakers, business owners c level people informed decisions businesses. It includes massive data relating technological advancements, trending products services observed market. The major key pillars businesses explained concise manner effectively fueling progress market. Our Study Report Offers: Market share analysis regional countrylevel segments. Machine Learning Data Catalog Software Market share analysis best business players. Strategic proposal new entrants. Market forecasts years mentioned segments, subsegments conjointly regional markets. Market Opportunities, Trends, Constraints, Threats, Challenges, Drivers, Investment suggestions. Strategic steerage key business segments supported market estimations. Competitive landscaping mapping key common trends. Company identification careful methods, financials, far developments. provide chain trends mapping foremost recent technological advancements. The reports conclusion reveals overall scope Global Machine Learning Data Catalog Software Market terms feasibility investments segments market, descriptive passage outlines feasibility new projects succeed market near future. If special requirement let know offer report want. Talk Our Analyst Special RequirementCustomization report: Media Relations Mr. Shah CEOStratagem Market Insights14158710703 Email Web Profile Follow Stratagem Market Insights Stratagem Market Insights Logo Site Preview: Visit Full Site Close Preview Source: Stratagem Market Insights Posted Friday, March 06, 2020 10:49 AM CST Permalink For information content press release contact media relations contact listed directly. Security Policy Report Abuse 2005 2020 SBWire, service ReleaseWire LLC Important Disclaimer Customer Support Knowledgebase Submit ticket

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