{"id":3117,"date":"2020-06-18T08:00:38","date_gmt":"2020-06-18T08:00:38","guid":{"rendered":"https:\/\/news.sap.com\/denmark\/?p=3117"},"modified":"2024-08-01T19:07:13","modified_gmt":"2024-08-01T17:07:13","slug":"sa-er-det-ikke-svaerere-hul-pa-bylden-og-i-gang-med-ml","status":"publish","type":"post","link":"https:\/\/news.sap.com\/denmark\/2020\/06\/sa-er-det-ikke-svaerere-hul-pa-bylden-og-i-gang-med-ml\/","title":{"rendered":"S\u00e5 er det ikke sv\u00e6rere: hul p\u00e5 bylden og i gang med ML!"},"content":{"rendered":"

Klumme. S\u00e5 skete det. Jeg fik hul p\u00e5 bylden og lavede min f\u00f8rste Python-scriptede algoritme. Pludselig var tiden, opgaven og m\u00e5nens stilling den helt rette. Virksomheder kan sagtens komme med p\u00e5 rejsen!<\/p>\n

Wauw. Pludselig l\u00e5 hele verden \u00e5ben for mig. S\u00e5dan f\u00f8ltes det i hvert fald for et kort \u00f8jeblik. Som en erfaren datah\u00e5ndv\u00e6rker, dataingeni\u00f8r og informationsarkitekt var det sk\u00f8nt at st\u00e5 fadder til sin f\u00f8rste ML-algoritme og se den levere. En lille prototype \u2013 selvf\u00f8lgelig \u2013 men den satte straks hjernen i gang med at finde p\u00e5 nye m\u00e5der at bruge Machine Learning p\u00e5.<\/p>\n

En uge senere\u2026<\/strong><\/h2>\n

Det lille skub kom da en af de studerende i vores data lab i SAP Experience Centeret arbejdede p\u00e5 en algoritme til at bestemme en films genre. Det uforpligtende projekt m\u00e5tte jeg kaste mig ind i og p\u00e5 en uge var vi flyttet fra ide til kode og f\u00e6rdig service. Processen rummer \u2013 i allergroveste tr\u00e6k – at man skal finde og udvikle Python-koden, og man skal s\u00e6tte den sammen med data, og s\u00e5 skal man s\u00e6tte hele herligheden i produktion.<\/p>\n

Jeg taler ML<\/strong><\/h2>\n

En stor forn\u00f8jelse og en AHA-oplevelse. Der er sagt og skrevet s\u00e5 meget om kunstig intelligens, og derfor kan det f\u00f8les som et gennembrud at komme fra det overordnede meta-niveau til rent faktisk at udtrykke sig p\u00e5 Swahili \u2026 jeg mener ML. Og s\u00e5 er det heller ikke sv\u00e6rere!<\/p>\n

I virksomhederne kan der v\u00e6re en tendens til at overkomplicere dette felt. Enten sker der absolut intet eller ogs\u00e5 bliver projektet med at indt\u00e6nke \u201dkunstig intelligens\u201d s\u00e5 mastodontisk, at det ikke rykker sig ud af fl\u00e6kken eller l\u00f8ber ind i diverse organisatoriske og ressourcem\u00e6ssige problemer.<\/p>\n

Men over lange str\u00e6kninger er det faktisk s\u00e5dan, at vi kan genbruge meget af den organisation og viden, som vi bruger p\u00e5 n\u00e6sten alle andre omr\u00e5der, n\u00e5r vi skal udrulle it-l\u00f8sninger i organisationer.<\/p>\n

Start med use case<\/strong><\/h2>\n

For det f\u00f8rste m\u00e5 og skal vi starte med et forretningsm\u00e6ssigt problem. En proces, et produkt eller noget andet, som vi vil forbedre. Denne use case skal v\u00e6re virkelig og den skal ejes af forretningen. Uanset om vi vil bruge SAP eller andre l\u00f8sninger til at l\u00f8se problemet.<\/p>\n

CBS\u2019eren, h\u00e5ndv\u00e6rkeren og arkitekten<\/strong><\/h2>\n

For det andet skal projektet have et s\u00e6t gammelkendte kompetencer eller roller d\u00e6kket ind. Udover den klassiske forretningsejer \u2013 lad os kalde hende CBS\u2019eren \u2013 skal vi have en data scientist og en arkitekt.<\/p>\n

CBS\u2019eren ejer casen og vurderer v\u00e6rdien og retningen i projektet. Data scientisten er datah\u00e5ndv\u00e6rkeren, som arbejder med data og med kodning. Mellem de to kernekompetencer har vi brug for en brobygger. En arkitekt eller ingeni\u00f8r, som kan etablere det samlede milj\u00f8 og den infrastruktur, som er n\u00f8dvendig for at n\u00e5 m\u00e5let. Disse tre kompetencetyper skal v\u00e6re der, men de beh\u00f8ver naturligvis ikke at v\u00e6re fordelt p\u00e5 tre personer.<\/p>\n

Langtidsholdbart og replikerbart<\/strong><\/h2>\n

For det tredje skal det enkelte ML-projekt ses som \u00e9t i en r\u00e6kke, s\u00e5 erfaringen opsamles gennem en struktur, der giver en virksomheden en langtidsholdbar tilgang til data og avanceret analyse. M\u00e5let er at opbygge et organisatorisk milj\u00f8, s\u00e5 l\u00f8sninger kan knopskyde og befrugte hinanden, og s\u00e5 man undg\u00e5r at starte fra scratch hver gang. Ellers er der overh\u00e6ngende risiko for, at erfaringen bliver for \u201dh\u00e5ndholdt\u201d og dermed flygtig og sv\u00e6r at replikere.<\/p>\n

Ikke meget i dette er nyt. Tv\u00e6rtimod. Vi skal ikke g\u00f8re ML sv\u00e6rere end det er. Jeg var m\u00e5ske selv faldet lidt ned i den gr\u00f8ft, og det var dejligt at komme over p\u00e5 den anden side.<\/p>\n

ML til at svare kunder?<\/strong><\/h2>\n

Det lille pilotprojekt har for \u00f8vrigt kastet en ide af sig. Vi f\u00e5r mange kundehenvendelser som enten er Request For Information eller Request For Proposals, og en del af besvarelsen er et s\u00e6t af standardinformation om protokoller, rapporteringsformater og underliggende teknologi-komponenter. Derfor er der ofte en stor f\u00e6llesm\u00e6ngde i svarene. Ideen er den simple at lade en ML-algoritme l\u00e6re af vores seneste besvarelser, og derefter automatisk levere udkast til det nye svar, som vi skal give til en kunde.<\/p>\n

Kan godt ske jeg stadig er en amat\u00f8r, men jeg har troen p\u00e5 at det kan lade sig g\u00f8re.<\/p>\n

 <\/p>\n

Af Thomas Madsen, Head of SAP Data Lab Copenhagen in the SAP Experience Center Copenhagen<\/strong><\/em><\/p>\n

\"\"<\/p>\n

Denne klumme er oprindeligt publiceret i Computerworld den 4. juni 2020.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Klumme. S\u00e5 skete det. Jeg fik hul p\u00e5 bylden og lavede min f\u00f8rste Python-scriptede algoritme. Pludselig var tiden, opgaven og m\u00e5nens stilling den helt rette….<\/p>\n","protected":false},"author":3000,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[4522668,4579962],"tags":[5565133,1495444,5562838,5565134,5564415,4645745,5564662],"sapn-display":[5566077,5566075,45149,4579516,60294],"sapn-type":[5563396],"class_list":["post-3117","post","type-post","status-publish","format-standard","hentry","category-teknologi","category-virksomhedsinformation","tag-algoritme","tag-machine-learning","tag-ml","tag-python-scriptede-algoritme","tag-sap-data-lab-copenhagen","tag-sap-experience-center-copenhagen","tag-thomas-madsen","sapn-display-artikel-hero","sapn-display-artikel-spotlight","sapn-display-homepage-hero","sapn-display-top-stories","sapn-display-hero","sapn-type-artikel"],"yoast_head":"\nS\u00e5 er det ikke sv\u00e6rere: hul p\u00e5 bylden og i gang med ML! - SAP Danmark Nyheder<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/news.sap.com\/denmark\/2020\/06\/sa-er-det-ikke-svaerere-hul-pa-bylden-og-i-gang-med-ml\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"S\u00e5 er det ikke sv\u00e6rere: hul p\u00e5 bylden og i gang med ML!\" \/>\n<meta property=\"og:description\" content=\"Klumme. 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S\u00e5 skete det. Jeg fik hul p\u00e5 bylden og lavede min f\u00f8rste Python-scriptede algoritme. 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