EN Deploying a reinforcement learning policy in real-world operational systems might make some stakeholders wonder how they will ensure that those systems remain stable
"deploying a reinforcement" သို့ ကိုးရီးယား သို့ဘာသာပြန်ပါ
အင်္ဂလိပ်စာ မှ ကိုးရီးယား သို့စကားစု "deploying a reinforcement" ကိုဘာသာပြန်ဆိုထားသော 50 ဘာသာပြန်ဆိုမှု၏ 50 ကိုပြထားသည်။
deploying a reinforcement ၏ အင်္ဂလိပ်စာ မှ ကိုးရီးယား သို့ ဘာသာပြန်ခြင်း
အင်္ဂလိပ်စာ
ကိုးရီးယား
KO 실제 운영 시스템에 강화학습 정책을 배포하는 것에 대해 고객들은 기존 시스템과의 안정성에 의문을 가질 수 있습니다
စကားအသုံးအနှုန်း silje un-yeong siseutem-e ganghwahagseub jeongchaeg-eul baepohaneun geos-e daehae gogaegdeul-eun gijon siseutemgwaui anjeongseong-e uimun-eul gajil su issseubnida
EN Machine LearningArtificial IntelligenceNew TechnologyAutomotiveDeep LearningAutonomous VehiclesSelf-Driving VehiclesAutonomous CarsAutomated VehiclesDeep Reinforcement LearningArtificial Intelligence Applications
KO 기계학습Artificial IntelligenceNew Technology자동차딥러닝자율주행차자가 운전 차자율주행차Automated VehiclesDeep Reinforcement Learning인공지능 어플리케이션
စကားအသုံးအနှုန်း gigyehagseubArtificial IntelligenceNew Technologyjadongchadibleoningjayuljuhaengchajaga unjeon chajayuljuhaengchaAutomated VehiclesDeep Reinforcement Learning-ingongjineung eopeullikeisyeon
EN Pathmind is a SaaS platform that enables businesses to apply reinforcement learning to real-world scenarios without data science expertise
KO Pathmind는 기업이 데이터 과학 전문 지식 없이도 실제 시나리오에 강화학습을 적용할 수 있도록 지원하는 SaaS 플랫폼입니다
စကားအသုံးအနှုန်း Pathmindneun gieob-i deiteo gwahag jeonmun jisig eobs-ido silje sinalio-e ganghwahagseub-eul jeog-yonghal su issdolog jiwonhaneun SaaS peullaespom-ibnida
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| saas | saas |
EN Pathmind is designed for professional simulation engineers, consultancies, and corporate teams who seek to apply reinforcement learning to their simulation projects.
KO Pathmind는 시뮬레이션 프로젝트에 강화학습을 적용하고자 하는 전문 시뮬레이션 엔지니어, 컨설턴트 및 기업을 위해 설계되었습니다.
စကားအသုံးအနှုန်း Pathmindneun simyulleisyeon peulojegteue ganghwahagseub-eul jeog-yonghagoja haneun jeonmun simyulleisyeon enjinieo, keonseolteonteu mich gieob-eul wihae seolgyedoeeossseubnida.
EN Experiment with state-of-the-art reinforcement learning algorithms and techniques without data science expertise. We handle it for you.
KO 고객에게 데이터 과학 전문 지식 없이도 최첨단 강화학습 알고리즘과 기술을 적용할 수 있는 실험 환경을 제공합니다.
စကားအသုံးအနှုန်း gogaeg-ege deiteo gwahag jeonmun jisig eobs-ido choecheomdan ganghwahagseub algolijeumgwa gisul-eul jeog-yonghal su issneun silheom hwangyeong-eul jegonghabnida.
EN Pathmind automatically handles the pain of integrating a simulation with reinforcement learning libraries, hyperparameter tuning, and setting up training infrastructure.
KO Pathmind는 고객의 시뮬레이션과 강화학습 라이브러리, 하이퍼 파라미터 튜닝 및 트레이닝 인프라 구축과의 통합을 자동으로 관리합니다.
စကားအသုံးအနှုန်း Pathmindneun gogaeg-ui simyulleisyeongwa ganghwahagseub laibeuleoli, haipeo palamiteo tyuning mich teuleining inpeula guchuggwaui tonghab-eul jadong-eulo gwanlihabnida.
EN Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API
KO 사용이 간편한 Python API를 통해 강화 학습, 모방 학습, 신경 진화 및 기타 머신러닝 방법을 사용하여 에이전트를 교육할 수 있습니다
စကားအသုံးအနှုန်း sayong-i ganpyeonhan Python APIleul tonghae ganghwa hagseub, mobang hagseub, singyeong jinhwa mich gita meosinleoning bangbeob-eul sayonghayeo eijeonteuleul gyoyughal su issseubnida
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| api | api |
EN Pathmind provides an easy-to-use plugin that inserts reinforcement learning tools into your simulation.
KO Pathmind는 고객의 시뮬레이션에 강화학습 도구를 설치하는 사용하기 편리한 플러그인을 제공합니다.
စကားအသုံးအနှုန်း Pathmindneun gogaeg-ui simyulleisyeon-e ganghwahagseub doguleul seolchihaneun sayonghagi pyeonlihan peulleogeu-in-eul jegonghabnida.
EN Pathmind provides a simple interface that enables you to convert simulation data into a format that a reinforcement learning algorithm can understand. No AI expertise or PhD needed.
KO Pathmind는 시뮬레이션 데이터를 강화학습 알고리즘이 이해할 수 있는 포맷으로 변환하는 간편한 인터페이스를 제공합니다. AI에 관한 전문 지식이나 전문 인력 없이도 사용 가능합니다.
စကားအသုံးအနှုန်း Pathmindneun simyulleisyeon deiteoleul ganghwahagseub algolijeum-i ihaehal su issneun pomaes-eulo byeonhwanhaneun ganpyeonhan inteopeiseuleul jegonghabnida. AIe gwanhan jeonmun jisig-ina jeonmun inlyeog eobs-ido sayong ganeunghabnida.
EN Pathmind trains your policy using the latest reinforcement learning algorithms such as PPO. We constantly test and update algorithms so you will always have access to the state-of-the-art.
KO Pathmind는 PPO와 같은 최신 강화 학습 알고리즘을 사용하여 정책을 훈련시킵니다. 또한 항상 최신 기술을 이용할 수 있도록 알고리즘을 지속적으로 테스트 및 업데이트합니다.
စကားအသုံးအနှုန်း Pathmindneun PPOwa gat-eun choesin ganghwa hagseub algolijeum-eul sayonghayeo jeongchaeg-eul hunlyeonsikibnida. ttohan hangsang choesin gisul-eul iyonghal su issdolog algolijeum-eul jisogjeog-eulo teseuteu mich eobdeiteuhabnida.
EN No reinforcement learning knowledge or cloud infrastructure expertise is required. Pathmind handles this for you so you can focus on improving your simulation.
KO 강화학습이나 클라우드 전문 지식이 필요하지 않습니다. Pathmind는 사용자가 시뮬레이션을 개선하는 데 집중할 수 있도록 이를 처리합니다.
စကားအသုံးအနှုန်း ganghwahagseub-ina keullaudeu jeonmun jisig-i pil-yohaji anhseubnida. Pathmindneun sayongjaga simyulleisyeon-eul gaeseonhaneun de jibjunghal su issdolog ileul cheolihabnida.
EN A reinforcement learning policy can dynamically adjust to variability in the system
KO 강화학습 정책은 시스템의 가변성을 동적으로 조정할 수 있습니다
စကားအသုံးအနှုန်း ganghwahagseub jeongchaeg-eun siseutem-ui gabyeonseong-eul dongjeog-eulo jojeonghal su issseubnida
EN The nature of reinforcement learning makes this task straightforward, especially in coordinating the action of many machines together.
KO 강화학습은 여러 기계들의 동작을 함께 조정하여 이 작업을 더욱 간단하고 명료하게 만든다는 특징이 있습니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun yeoleo gigyedeul-ui dongjag-eul hamkke jojeonghayeo i jag-eob-eul deoug gandanhago myeonglyohage mandeundaneun teugjing-i issseubnida.
EN Compared to heuristics that typically optimize one KPI at a time, a reinforcement learning policy can be trained to optimize multiple, independent KPIs simultaneously, even in complex scenarios
KO 일반적으로 한 번에 하나의 KPI를 최적화하는 휴리스틱 방법에 비해, 강화학습 정책은 복잡한 시나리오에서도 다수의 독립적인 KPI를 동시에 최적화하도록 훈련될 수 있습니다
စကားအသုံးအနှုန်း ilbanjeog-eulo han beon-e hanaui KPIleul choejeoghwahaneun hyuliseutig bangbeob-e bihae, ganghwahagseub jeongchaeg-eun bogjabhan sinalio-eseodo dasuui doglibjeog-in KPIleul dongsie choejeoghwahadolog hunlyeondoel su issseubnida
EN Instead of solely maximizing revenue, a reinforcement policy simultaneously show how to minimize carbon emissions ? two seemingly competing objectives.
KO 강화학습 정책은 단지 수익을 극대화하는 것 외에도 또 하나의 목표인 탄소 배출을 최소화하는 방법을 동시에 제시합니다.
စကားအသုံးအနှုန်း ganghwahagseub jeongchaeg-eun danji su-ig-eul geugdaehwahaneun geos oeedo tto hanaui mogpyoin tanso baechul-eul choesohwahaneun bangbeob-eul dongsie jesihabnida.
EN Instead of jumping to deployment, a reinforcement learning policy can be used to enhance an existing heuristic in a working system.
KO 한 가지 해결책으로, 기존 시스템에 정책을 배포하는 대신 현재 작동하는 시스템의 휴리스틱을 향상시키는 방법으로 강화학습 정책을 사용할 수 있습니다.
စကားအသုံးအနှုန်း han gaji haegyeolchaeg-eulo, gijon siseutem-e jeongchaeg-eul baepohaneun daesin hyeonjae jagdonghaneun siseutem-ui hyuliseutig-eul hyangsangsikineun bangbeob-eulo ganghwahagseub jeongchaeg-eul sayonghal su issseubnida.
EN Pathmind allows you to experiment with state-of-the-art tools from the reinforcement learning ecosystem out of box.
KO Pathmind를 사용하면 강화학습의 최신의 기술을 즉시 사용할 수 있습니다.
စကားအသုံးအနှုန်း Pathmindleul sayonghamyeon ganghwahagseub-ui choesin-ui gisul-eul jeugsi sayonghal su issseubnida.
EN A simple plugin to add reinforcement learning to existing simulations.
KO 기존 시뮬레이션에 강화학습을 추가하는 간단한 플러그인
စကားအသုံးအနှုန်း gijon simyulleisyeon-e ganghwahagseub-eul chugahaneun gandanhan peulleogeu-in
EN Support for single and multiple reinforcement learning agents.
KO 싱글 및 멀티 강화 학습 에이전트 지원
စကားအသုံးအနှုန်း sing-geul mich meolti ganghwa hagseub eijeonteu jiwon
EN Skeptical about reinforcement learning? Try our examples below to see for yourself. All simulations are built using AnyLogic.
KO 강화학습에 회의적이신가요? 아래의 예제를 통해 직접 확인해보세요. 모든 시뮬레이션은 AnyLogic을 사용하여 구현되었습니다.
စကားအသုံးအနှုန်း ganghwahagseub-e hoeuijeog-isingayo? alaeui yejeleul tonghae jigjeob hwag-inhaeboseyo. modeun simyulleisyeon-eun AnyLogiceul sayonghayeo guhyeondoeeossseubnida.
EN Compared to routine maintenance and predictive failure heuristics, Pathmind?s reinforcement learning achieves 58% more total profit.
KO 일상적인 유지보수 및 예측에 실패하는 휴리스틱 방법에 비해, Pathmind의 강화학습은 58% 더 높은 총수익을 달성합니다.
စကားအသုံးအနှုန်း ilsangjeog-in yujibosu mich yecheug-e silpaehaneun hyuliseutig bangbeob-e bihae, Pathmindui ganghwahagseub-eun 58% deo nop-eun chongsu-ig-eul dalseonghabnida.
EN This model simulates a fulfillment center that uses Reinforcement Learning to move boxes from one place to another and obtain the maximum performance of its workers
KO 본 예제는 물류센터에서 강화학습을 활용해 제품 상자를 이동하고, 작업자의 최대 성과를 얻을 수 있도록 시뮬레이션한 모델입니다
စကားအသုံးအနှုန်း bon yejeneun mullyusenteoeseo ganghwahagseub-eul hwal-yonghae jepum sangjaleul idonghago, jag-eobjaui choedae seong-gwaleul eod-eul su issdolog simyulleisyeonhan model-ibnida
EN It highlights the advantages of using Reinforcement Learning by Pathmind in a simulation model to obtain better results in a dynamic and complex environment.
KO Pathmind의 강화학습을 사용하여 동적이고 복잡한 환경에서 더 나은 결과를 도출할 수 있었습니다.
စကားအသုံးအနှုန်း Pathmindui ganghwahagseub-eul sayonghayeo dongjeog-igo bogjabhan hwangyeong-eseo deo na-eun gyeolgwaleul dochulhal su iss-eossseubnida.
EN Reinforcement learning learns to strategically arrange pallets closest to their expected final destination.
KO 강화학습은 예상되는 최종 목표에 가장 가까운 팔레트를 전략적으로 처리하는 방법을 학습합니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun yesangdoeneun choejong mogpyoe gajang gakkaun palleteuleul jeonlyagjeog-eulo cheolihaneun bangbeob-eul hagseubhabnida.
EN Reinforcement learning is able to complete manufacturing in fewer movements than the heuristic.
KO 강화학습은 휴리스틱 방법보다 더 적은 이동으로 제조 공정을 완료할 수 있었습니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun hyuliseutig bangbeobboda deo jeog-eun idong-eulo jejo gongjeong-eul wanlyohal su iss-eossseubnida.
EN But other physical stores employ the same strategy, using reinforcement learning to find the best location.
KO 그러나 경쟁 업체의 매장에서도 강화학습을 통하여 최상의 위치를 찾는 동일한 전략을 사용하고 있습니다.
စကားအသုံးအနှုန်း geuleona gyeongjaeng eobche-ui maejang-eseodo ganghwahagseub-eul tonghayeo choesang-ui wichileul chajneun dong-ilhan jeonlyag-eul sayonghago issseubnida.
EN This competitive dynamic allows the reinforcement learning policy to learn that if all competitors cluster close to one another near the center of the city, sales will be maximized for each individual store.
KO 이러한 경쟁 구도로 인해 강화학습 정책이 만약 모든 경쟁 업체들이 도심 주변에서 서로 근접하게 위치할 경우 각 업체의 매출이 최대화 되는 것을 학습하게 됩니다.
စကားအသုံးအနှုန်း ileohan gyeongjaeng gudolo inhae ganghwahagseub jeongchaeg-i man-yag modeun gyeongjaeng eobchedeul-i dosim jubyeon-eseo seolo geunjeobhage wichihal gyeong-u gag eobche-ui maechul-i choedaehwa doeneun geos-eul hagseubhage doebnida.
EN Reinforcement learning discovers the Nash Equilibrium for several stores.
KO 강화학습은 여러 매장들에 대한 내쉬 균형(Nash Equilibrium) 을 찾아냅니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun yeoleo maejangdeul-e daehan naeswi gyunhyeong(Nash Equilibrium) eul chaj-anaebnida.
EN Reinforcement learning outperforms the heuristic of sending goods to the nearest manufacturing center by over 80%, maximizing profit and minimizing wait times.
KO 강화학습은 가장 가까운 제조센터로 상품을 보내는 기존의 휴리스틱 방법보다 80% 이상 우수하며 수익을 극대화하고 대기 시간을 최소화합니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun gajang gakkaun jejosenteolo sangpum-eul bonaeneun gijon-ui hyuliseutig bangbeobboda 80% isang usuhamyeo su-ig-eul geugdaehwahago daegi sigan-eul choesohwahabnida.
EN Reinforcement learning outperforms the heuristic to increase throughput in the factory by 50%.
KO 강화학습은 휴리스틱 방법에 비해 공장의 처리량을 50% 증가시킵니다.
စကားအသုံးအနှုန်း ganghwahagseub-eun hyuliseutig bangbeob-e bihae gongjang-ui cheolilyang-eul 50% jeung-gasikibnida.
EN We use deep reinforcement learning, a form of cutting-edge AI, to do that
KO 이를 위해 최첨단 AI의 한 형태인 심층 강화학습을 사용합니다
စကားအသုံးအနှုန်း ileul wihae choecheomdan AIui han hyeongtaein simcheung ganghwahagseub-eul sayonghabnida
EN We compare the reinforcement learning policy with three call routing heuristics (no call transferring, shortest queue, and most efficient call center)
KO 강화학습 정책을 세 가지 통화 라우팅 휴리스틱(통화 전송 없음, 최단 대기열, 가장 효율적인 콜 센터)과 비교합니다
စကားအသုံးအနှုန်း ganghwahagseub jeongchaeg-eul se gaji tonghwa lauting hyuliseutig(tonghwa jeonsong eobs-eum, choedan daegiyeol, gajang hyoyuljeog-in kol senteo)gwa bigyohabnida
EN The reinforcement learning policy trained using Pathmind outperforms the heuristics by over 9.6%.
KO Pathmind의 강화학습 정책은 휴리스틱 방법을 9.6% 이상 능가합니다.
စကားအသုံးအနှုန်း Pathmindui ganghwahagseub jeongchaeg-eun hyuliseutig bangbeob-eul 9.6% isang neung-gahabnida.
EN Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API
KO 사용이 간편한 Python API를 통해 강화 학습, 모방 학습, 신경 진화 및 기타 머신러닝 방법을 사용하여 에이전트를 교육할 수 있습니다
စကားအသုံးအနှုန်း sayong-i ganpyeonhan Python APIleul tonghae ganghwa hagseub, mobang hagseub, singyeong jinhwa mich gita meosinleoning bangbeob-eul sayonghayeo eijeonteuleul gyoyughal su issseubnida
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| api | api |
EN Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning
KO 기계 학습의 주요 영역인 지도 학습, 자율 학습, 심층 학습 및 강화 학습에 대한 업무 역량을 개발하세요
စကားအသုံးအနှုန်း gigye hagseub-ui juyo yeong-yeog-in jido hagseub, jayul hagseub, simcheung hagseub mich ganghwa hagseub-e daehan eobmu yeoglyang-eul gaebalhaseyo
EN It enables brand consistency through automatic asset updates and reinforcement of brand guidelines, providing a single source of truth within businesses and a more consistent user experience to external audiences
KO 자동 자산 업데이트 및 브랜드 지침 강화를 통해 브랜드 일관성을 지원하여 기업 내에서는 단일 정보 소스를 제공하고 외부 잠재고객에게는 보다 일관된 사용자 경험을 제공합니다
စကားအသုံးအနှုန်း jadong jasan eobdeiteu mich beulaendeu jichim ganghwaleul tonghae beulaendeu ilgwanseong-eul jiwonhayeo gieob naeeseoneun dan-il jeongbo soseuleul jegonghago oebu jamjaegogaeg-egeneun boda ilgwandoen sayongja gyeongheom-eul jegonghabnida
EN Whether you are storing objects, deploying serverless code, or blocking cyber attacks, all our security and performance capabilities extend globally, making configuration a breeze.
KO 개체의 저장, 서버리스 코드의 배포, 사이버 공격의 차단 등 모든 일에서 당사의 보안 및 성능 기능은 전세계적으로 확장되며 구성이 매우 용이합니다.
စကားအသုံးအနှုန်း gaeche-ui jeojang, seobeoliseu kodeuui baepo, saibeo gong-gyeog-ui chadan deung modeun il-eseo dangsaui boan mich seongneung gineung-eun jeonsegyejeog-eulo hwagjangdoemyeo guseong-i maeu yong-ihabnida.
EN “Cloudflare, in conjunction with a few providers, makes deploying and running our site very light and easy. I would recommend Cloudflare to anyone looking to solve the problems we have.”
KO “Cloudflare는 몇몇 공급자와 함께 저희 사이트의 배포와 실행을 아주 가볍고 간편하게 만들어 주었습니다. 저희와 같은 문제의 해결책을 찾고 있는 모두에게 Cloudflare를 추천하고 싶습니다.”
စကားအသုံးအနှုန်း “Cloudflareneun myeochmyeoch gong-geubjawa hamkke jeohui saiteuui baepowa silhaeng-eul aju gabyeobgo ganpyeonhage mandeul-eo jueossseubnida. jeohuiwa gat-eun munje-ui haegyeolchaeg-eul chajgo issneun moduege Cloudflareleul chucheonhago sipseubnida.”
EN Enforce geo-based access policies or lower latency by deploying serverless code on Cloudflare's network in 200+ cities in over 100 countries.
KO 100개 이상 국가의 200개 이상 도시에 있는 Cloudflare의 네트워크에 서버리스 코드를 배포하여 지리적 위치에 따른 액세스 정책을 실행하고 대기 시간을 단축하세요.
စကားအသုံးအနှုန်း 100gae isang guggaui 200gae isang dosie issneun Cloudflare-ui neteuwokeue seobeoliseu kodeuleul baepohayeo jilijeog wichie ttaleun aegseseu jeongchaeg-eul silhaenghago daegi sigan-eul danchughaseyo.
EN Deploying applications using containers provides an effective way to ensure that application code runs in a consistent, lightweight, and portable environment
KO 컨테이너를 사용한 애플리케이션 배포는 애플리케이션 코드를 경량의 휴대용 환경에서 일관성 있게 실행할 수 있는 효율적인 방법을 제공합니다
စကားအသုံးအနှုန်း keonteineoleul sayonghan aepeullikeisyeon baeponeun aepeullikeisyeon kodeuleul gyeonglyang-ui hyudaeyong hwangyeong-eseo ilgwanseong issge silhaenghal su issneun hyoyuljeog-in bangbeob-eul jegonghabnida
EN Easy build import from popular open source tools and native support for Git, Hg, and SVN means you'll be building and deploying like a champ.
KO 인기 오픈 소스 도구와 Git, Hg 및 SVN을 기본적으로 지원하여 빌드 가져오기가 쉬워져 빌드와 배포가 간편해졌습니다.
စကားအသုံးအနှုန်း ingi opeun soseu doguwa Git, Hg mich SVNeul gibonjeog-eulo jiwonhayeo bildeu gajyeoogiga swiwojyeo bildeuwa baepoga ganpyeonhaejyeossseubnida.
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| git | git |
| svn | svn |
EN A container image registry for building, distributing, and deploying containers.
KO 컨테이너의 구축, 분산, 배포에 사용할 수 있는 컨테이너 이미지 레지스트리
စကားအသုံးအနှုန်း keonteineoui guchug, bunsan, baepo-e sayonghal su issneun keonteineo imiji lejiseuteuli
EN Deploying log management in context and at scale has never been faster, easier, or more attainable
KO 이제 그 어느 때보다 빠르고 쉽게, 또 맥락에 맞게 대규모의 로그를 관리할 수 있게 되었습니다
စကားအသုံးအနှုန်း ije geu eoneu ttaeboda ppaleugo swibge, tto maeglag-e majge daegyumoui logeuleul gwanlihal su issge doeeossseubnida
EN Say goodbye to managing updates and deploying patches. Just sit back, relax and enjoy as the latest features roll out with no downtime.
KO 더 이상 업데이트를 관리하고 패치를 배포할 필요가 없습니다. 업무 중단 없이 편리하게 최신 기능을 활용할 수 있습니다.
စကားအသုံးအနှုန်း deo isang eobdeiteuleul gwanlihago paechileul baepohal pil-yoga eobs-seubnida. eobmu jungdan eobs-i pyeonlihage choesin gineung-eul hwal-yonghal su issseubnida.
EN Find detailed product and feature information across releases, including use cases and tips for deploying and configuring Tableau.
KO Tableau 배포 및 구성에 관한 사용 사례와 팁을 비롯해, 다양한 릴리스에 대한 자세한 제품 및 기능 정보를 찾아보십시오.
စကားအသုံးအနှုန်း Tableau baepo mich guseong-e gwanhan sayong salyewa tib-eul biloshae, dayanghan lilliseue daehan jasehan jepum mich gineung jeongboleul chaj-abosibsio.
EN By quickly deploying 1Password to everyone in the organization, Dribbble now has peace of mind when sharing passwords, financial information, and documentation throughout the company..
KO 조직 내 모든 사람에게 1Password를 빠르게 배포함으로써 Dribbble은 이제 회사에서 비밀번호, 재무 정보 및 문서를 안심하고 공유할 수 있게 되었습니다.
စကားအသုံးအနှုန်း jojig nae modeun salam-ege 1Passwordleul ppaleuge baepoham-eulosseo Dribbbleeun ije hoesa-eseo bimilbeonho, jaemu jeongbo mich munseoleul ansimhago gong-yuhal su issge doeeossseubnida.
EN The IBM Cloud team, for example, uses Istio to address the control, visibility, and security issues it has encountered while deploying Kubernetes at massive scale. More specifically, Istio helps IBM:
KO 예를 들어, IBM Cloud 팀은 Istio를 사용해, 쿠버네티스를 대규모로 배포하는 동안 발생하는 제어, 가시성 및 보안 문제를 해결합니다. Istio는 IBM을 다음과 같은 방식으로 지원합니다.
စကားအသုံးအနှုန်း yeleul deul-eo, IBM Cloud tim-eun Istioleul sayonghae, kubeonetiseuleul daegyumolo baepohaneun dong-an balsaenghaneun jeeo, gasiseong mich boan munjeleul haegyeolhabnida. Istioneun IBMeul da-eumgwa gat-eun bangsig-eulo jiwonhabnida.
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| ibm | ibm |
EN Combine this with 15 times faster throughput and a 300% reduction in latency since migrating to AWS and deploying New Relic, and you can’t help but have happier customers
KO 이러한 결과에, AWS로 마이그레이션하고 뉴렐릭을 배포한 이후, 15배 늘어난 처리량과 300% 감소된 레이턴시를 결합하면, 고객 만족도가 높아질 수 밖에 없습니다
စကားအသုံးအနှုန်း ileohan gyeolgwa-e, AWSlo maigeuleisyeonhago nyulellig-eul baepohan ihu, 15bae neul-eonan cheolilyang-gwa 300% gamsodoen leiteonsileul gyeolhabhamyeon, gogaeg manjogdoga nop-ajil su bakk-e eobs-seubnida
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| aws | aws |
EN LiveLab: Deploying Microservices on Kubernetes in OCI
KO LiveLab: OCI에서 Kubernetes에 마이크로서비스 배포
စကားအသုံးအနှုန်း LiveLab: OCIeseo Kubernetes-e maikeuloseobiseu baepo
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| oci | oci |
EN Plan the networking topology for deploying SaaS applications on Oracle Cloud Infrastructure.
KO 오라클 클라우드 인프라스트럭쳐(OCI)에 SaaS 애플리케이션을 배포하기 위한 네트워킹 토폴로지를 계획합니다.
စကားအသုံးအနှုန်း olakeul keullaudeu inpeulaseuteuleogchyeo(OCI)e SaaS aepeullikeisyeon-eul baepohagi wihan neteuwoking topollojileul gyehoeghabnida.
| အင်္ဂလိပ်စာ | ကိုးရီးယား |
|---|---|
| saas | saas |
ဘာသာပြန်ချက်များကို 50 မှ 50 ကိုပြနေသည်
အလားတူ ဘာသာပြန်များ
deploying a reinforcement reinforcement reinforcement learning reinforcement learning algorithm reinforcement learning outperforms carbon fiber reinforcement nature of reinforcement reinforcement learning algorithms skeptical about reinforcement reinforcement of wcms reinforcement machine learning apply reinforcement learning reinforcement learning policy ideal sound reinforcement latest reinforcement learning deploying deploying serverless code deploying data deploying quickly makes deploying customers deploying deploying applications deploying analytic deploying software deploying ai concerns by deploying deploying computing power deploying thales identity deploying keysight solutions deploying custom error when deploying deploying active deploying log management deploying models deploying container building and deploying deploying kubernetes deploying data center deploying your digital deploying containerized applications deploying infrastructure deploying the right deploying applications using deploying using bitbucket started with deploying deploying patches organisations are deploying deploying different authentication deploying emulated workloads deploying their red
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