BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Opus Kinetic - ECPv6.15.12.2//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Opus Kinetic X-ORIGINAL-URL:https://www.opuskinetic.com X-WR-CALDESC:Events for Opus Kinetic REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Asia/Singapore BEGIN:STANDARD TZOFFSETFROM:+0800 TZOFFSETTO:+0800 TZNAME:+08 DTSTART:20240101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20251013 DTEND;VALUE=DATE:20251017 DTSTAMP:20260525T211811 CREATED:20250410T080719Z LAST-MODIFIED:20250410T081700Z UID:10002109-1760313600-1760659199@www.opuskinetic.com SUMMARY:AI Mastery in Upstream: Neural Networks\, Deep Learning\, and MLOps DESCRIPTION:Why Choose this Training Course\nThis hands-on upstream AI training course is crafted to provide participants with in-depth knowledge and practical experience in key Deep Learning techniques\, including Reinforcement Learning\, Generative Adversarial Networks (GANs)\, and Large Language Models (LLMs). Blending theoretical insights with practical exercises based on real-world Oil & Gas datasets\, the upstream AI training eural enables participants to develop the skills needed to confidently apply Deep Learning in solving everyday challenges within the Oil & Gas industry. \nThis upstream AI training offers a deep dive into Deep Learning and MLOps tailored for Oil & Gas professionals. Participants begin with foundational knowledge in machine learning\, TensorFlow\, and MLOps\, learning to build and optimize feedforward neural networks. The course progresses into convolutional neural networks\, object detection\, and image segmentation\, applied to tasks like pump monitoring and satellite image analysis. Time-series modeling\, anomaly detection\, and Bayesian networks are explored with applications in ESP maintenance and reservoir forecasting. Advanced natural language processing techniques such as text classification\, summarization\, and Named Entity Recognition are applied to industry texts\, with a focus on building user-friendly interfaces. The final day of this upstream AI training covers cutting-edge techniques including reinforcement learning for well placement and generative AI—using GANs and LLMs—for creating synthetic data and extracting insights from unstructured documents. Through hands-on exercises and real-world datasets\, participants gain practical skills to build\, deploy\, and manage robust AI solutions in upstream operations. \nWho Should Attend\nA reservoir engineer\, geologist\, petrophysicist\, or production/drilling engineer with programming experience and foundational knowledge of data science and machine learning\, looking to build a strong grasp of neural networks\, deep learning\, and machine learning operations (MLOps). \nKey Learning Objectives\n\nRecognizing opportunities to apply Deep Learning techniques within your area of expertise\nMaking informed choices when selecting appropriate machine learning approaches for specific challenges\nUnderstanding fundamental Deep Learning algorithms and how to implement them using TensorFlow and Keras\nApplying key machine learning methods to practical\, real-world scenarios in the Oil & Gas industry\nKey Deep Learning algorithms will be explored in depth\, supported by a variety of reusable code examples drawn from real-world Oil & Gas datasets.\nMLOps principles will also be covered\, providing guidance on developing complete end-to-end machine learning solutions—from defining project scope and training models to deploying them and creating user-friendly graphical interfaces.\n\nEnquiry Form\n\n\n \n \n SalutationMr.Ms.Miss.Mrs.Mdm.Dr.Prof.Capt.-Name*\n \n \n \n First\n \n \n \n \n Last\n \n \n Phone*Email*\n \n Company*Current Location* \n \n \n AfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBonaire\, Sint Eustatius and SabaBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos IslandsColombiaComorosCongoCongo\, Democratic Republic of theCook IslandsCosta RicaCroatiaCubaCuraçaoCyprusCzechiaCôte d'IvoireDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEswatiniEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuernseyGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHoly SeeHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea\, Democratic People's Republic ofKorea\, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine\, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRomaniaRussian FederationRwandaRéunionSaint BarthélemySaint Helena\, Ascension and Tristan da CunhaSaint Kitts and NevisSaint LuciaSaint MartinSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth Georgia and the South Sandwich IslandsSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwedenSwitzerlandSyria Arab RepublicTaiwanTajikistanTanzania\, the United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkmenistanTurks and Caicos IslandsTuvaluTürkiyeUS Minor Outlying IslandsUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVenezuelaViet NamVirgin Islands\, BritishVirgin Islands\, U.S.Wallis and FutunaWestern SaharaYemenZambiaZimbabweÅland Islands\n Country\n \n \n How many people are you looking at?1 - 2 Standard Rates3 - 5 Group Discount>10 In-House Training\, Huge Savings!Undecided\, need more informationThis is just an approximate number. You can finalise it when you send in the registration form.\n \n Send me brochure\n Comments:CAPTCHA URL:https://www.opuskinetic.com/training/ai-mastery-in-upstream-neural-networks-deep-learning-and-mlops/ LOCATION:Kuala Lumpur\, Federal Territory of Kuala Lumpur\, Kuala Lumpur\, Malaysia CATEGORIES:Big Data, AI & Cybersecurity,Oil/Gas/Petrochemicals ATTACH;FMTTYPE=image/jpeg:https://www.opuskinetic.com/wp-content/uploads/2025/04/Firefly-high-technology-big-data-for-the-upstream-oil-and-gas-sector-93194-scaled.jpg ORGANIZER;CN="Opus Kinetic Pte Ltd":MAILTO:info@opuskinetic.com GEO:3.1384965;101.7099933 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20251022 DTEND;VALUE=DATE:20251024 DTSTAMP:20260525T211811 CREATED:20250611T081738Z LAST-MODIFIED:20251227T062022Z UID:10002147-1761091200-1761263999@www.opuskinetic.com SUMMARY:Rail Cyber Security DESCRIPTION:Why Choose this Training Course\nThe railway sector is facing a new challenge: the Network Information Security (NIS) regulations. According to a 2020 survey by the European Union Agency for Cybersecurity (ENISA)\, only 33% of rail operators of essential services (OES) have fully implemented defensive measures against cyber attacks\, as recommended by NIS regulations. This places their software under serious risk\, not to mention their lack of compliance with the new regulations. \nWhen security breaches occur\, the ripple effect throughout an organisation can be vast\, with consequences that are both financial and personal. There may also be implications concerning system safety and resilience. \nThis rail cybersecurity training is an introduction to the major themes of cybersecurity and will start you on a journey to the creation of a secure rail operation. After taking this course\, you will be able to communicate effectively\, make informed trade-offs\, assess risk\, improve defences\, and reduce vulnerabilities in your systems. \nThis rail cybersecurity training is based upon the new railway cybersecurity-specific CENELEC standards\, TS50701 and best practices from other OT and IT Cybersecurity benchmarks such as ISO27001\, IEC 624423 and the Australian Standard® AS 7770 Rail Cyber Security. \nWho Should Attend\nThis rail cybersecurity training is for railway business leaders\, managers\, railway inspectors\, railway legislators\, safety professionals\, planners\, information technology (IT) professionals\, resilience specialists and railway engineers tasked with making decisions that could impact the cyber resilience of technical and organisational systems. \nThe rail cybersecurity training is focused more towards railway Operational technology (OT)\, although it also covers IT issues\, particularly their security risks and strategies from ISO-27001. No prior knowledge of cybersecurity is required for this course. \nKey Learning Objectives\nThis rail cybersecurity training is based upon the new railway cybersecurity-specific CENELEC standards\, TS50701\, and best practices from other OT and IT Cybersecurity standards such as ISO-27001\, IEC 624423 and the Australian Standard® AS 7770 Rail Cyber Security. \n\nWhat is Cybersecurity? Putting it into the context of railway and transportation\nIdentify threats and vulnerabilities (such as cybersecurity\, safety and availability)\nDevelop mitigation actions for threats and vulnerabilities and recovery from potential consequences\nCybersecurity: What standards are available for dealing with threats\nAn understanding of TS70101 and how it can improve cybersecurity across the entire railway\n\nEnquiry Form\n\n \n \n SalutationMr.Ms.Miss.Mrs.Mdm.Dr.Prof.Capt.-Name*\n \n \n \n First\n \n \n \n \n Last\n \n \n Phone*Email*\n \n Company*Current Location* \n \n \n AfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBonaire\, Sint Eustatius and SabaBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos IslandsColombiaComorosCongoCongo\, Democratic Republic of theCook IslandsCosta RicaCroatiaCubaCuraçaoCyprusCzechiaCôte d'IvoireDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEswatiniEthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuernseyGuineaGuinea-BissauGuyanaHaitiHeard Island and McDonald IslandsHoly SeeHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKorea\, Democratic People's Republic ofKorea\, Republic ofKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacaoMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth MacedoniaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine\, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRomaniaRussian FederationRwandaRéunionSaint BarthélemySaint Helena\, Ascension and Tristan da CunhaSaint Kitts and NevisSaint LuciaSaint MartinSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth Georgia and the South Sandwich IslandsSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan MayenSwedenSwitzerlandSyria Arab RepublicTaiwanTajikistanTanzania\, the United Republic ofThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkmenistanTurks and Caicos IslandsTuvaluTürkiyeUS Minor Outlying IslandsUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVenezuelaViet NamVirgin Islands\, BritishVirgin Islands\, U.S.Wallis and FutunaWestern SaharaYemenZambiaZimbabweÅland Islands\n Country\n \n \n How many people are you looking at?1 - 2 Standard Rates3 - 5 Group Discount>10 In-House Training\, Huge Savings!Undecided\, need more informationThis is just an approximate number. You can finalise it when you send in the registration form.\n \n Send me brochure\n Comments:CAPTCHA URL:https://www.opuskinetic.com/training/rail-cybersecurity-training/2025-10-22/ LOCATION:Singapore\, Singapore CATEGORIES:Big Data, AI & Cybersecurity,Logistics and Supply Chain ATTACH;FMTTYPE=image/jpeg:https://www.opuskinetic.com/wp-content/uploads/2025/06/rail-cybersecurity-fi1-scaled.jpg ORGANIZER;CN="Opus Kinetic Pte Ltd":MAILTO:info@opuskinetic.com GEO:1.352083;103.819836 END:VEVENT END:VCALENDAR