{"id":122534,"date":"2026-10-14T12:30:00","date_gmt":"2026-10-14T10:30:00","guid":{"rendered":"https:\/\/aixia.se\/from-pilot-to-production-the-mlops-pitfalls-when-scaling-ai\/"},"modified":"2026-10-14T12:31:56","modified_gmt":"2026-10-14T10:31:56","slug":"from-pilot-to-production-the-mlops-pitfalls-when-scaling-ai","status":"publish","type":"post","link":"https:\/\/aixia.se\/en\/from-pilot-to-production-the-mlops-pitfalls-when-scaling-ai\/","title":{"rendered":"From Pilot to Production: The MLOps Pitfalls When Scaling AI"},"content":{"rendered":"<style>.aixia-post{color:#1f2933;font-size:17px;line-height:1.7}\n.aixia-post .lead{font-size:19px;line-height:1.65;color:#334155}\n.aixia-post h2{font-size:21px!important;line-height:1.3!important;margin:36px 0 12px!important;font-weight:650!important;letter-spacing:-.2px;color:#0f172a}\n.aixia-post h2:before{content:\"\";display:block;width:34px;height:3px;border-radius:2px;background:#0b5fff;margin-bottom:12px}\n.aixia-post p{margin:0 0 16px}\n.aixia-post .cta{background:#f0f6ff;border:1px solid #dbe7ff;border-left:4px solid #0b5fff;border-radius:10px;padding:16px 20px;margin:28px 0}\n.aixia-post .cta p{margin:0}\n.aixia-post ul,.aixia-post ol{padding-left:22px;margin:0 0 16px}\n.aixia-post li{margin:0 0 8px}\n.aixia-post table{width:100%;border-collapse:collapse;margin:20px 0;font-size:15px}\n.aixia-post th,.aixia-post td{border:1px solid #e5e7eb;padding:10px 12px;text-align:left;vertical-align:top}\n.aixia-post th{background:#f8fafc;font-weight:600}\n<\/style>\n<div class=\"aixia-post\">\n<p class=\"lead\">Att bygga en AI-modell som fungerar i en pilot \u00e4r numera ganska enkelt. Att f\u00e5 samma modell att fungera stabilt i produktion, m\u00e5nad efter m\u00e5nad, \u00e4r n\u00e5got helt annat. M\u00e5nga organisationer har i dag lyckade piloter bakom sig, men betydligt f\u00e4rre har tagit steget vidare till drift.<\/p>\n<p>Orsaken \u00e4r s\u00e4llan sj\u00e4lva modellen. Det som brister \u00e4r allt runt omkring. Ingen har riktig koll p\u00e5 vilken data och vilken modellversion som anv\u00e4nds, ingen m\u00e4rker n\u00e4r resultaten b\u00f6rjar f\u00f6rs\u00e4mras och det \u00e4r oklart vem som ansvarar n\u00e4r n\u00e5got f\u00f6r\u00e4ndras. H\u00e4r g\u00e5r vi igenom de vanligaste fallgroparna n\u00e4r AI ska skalas, och vad som kr\u00e4vs f\u00f6r att undvika dem.<\/p>\n<h2>Varf\u00f6r piloter fastnar<\/h2>\n<p>En pilot byggs ofta f\u00f6r att bevisa att n\u00e5got \u00e4r m\u00f6jligt: ett litet team, ett avgr\u00e4nsat dataset och en notebook p\u00e5 en arbetsstation eller en enskild GPU-server. Det \u00e4r ett bra s\u00e4tt att komma i g\u00e5ng, men det skapar ett arbetss\u00e4tt som inte h\u00e5ller n\u00e4r modellen ska anv\u00e4ndas p\u00e5 riktigt.<\/p>\n<p>I produktion g\u00e4ller andra villkor. Modellen ska g\u00e5 att uppdatera utan att n\u00e5got g\u00e5r s\u00f6nder, resultaten ska g\u00e5 att f\u00f6rklara och upprepa, och n\u00e5gon ska m\u00e4rka om kvaliteten f\u00f6rs\u00e4mras. Flera team ska kunna dela p\u00e5 samma infrastruktur, och kraven p\u00e5 s\u00e4kerhet och sp\u00e5rbarhet \u00f6kar. Det som fungerade f\u00f6r en datascientist fungerar s\u00e4llan f\u00f6r tio.<\/p>\n<p>Glappet mellan de tv\u00e5 v\u00e4rldarna \u00e4r precis det MLOps handlar om. Det \u00e4r inte ett verktyg utan ett s\u00e4tt att arbeta, d\u00e4r data, kod, modeller och infrastruktur hanteras med samma disciplin som annan mjukvara i produktion.<\/p>\n<h2>De fem vanligaste f\u00e4llorna<\/h2>\n<p>N\u00e4stan alla haverier vi ser g\u00e5r att h\u00e4rleda till samma fem omr\u00e5den.<\/p>\n<p><strong>1. Ingen koll p\u00e5 data och modellversion.<\/strong> Den vanligaste fr\u00e5gan som ingen kan svara p\u00e5 \u00e4r: vilken data tr\u00e4nades den h\u00e4r modellen p\u00e5? I m\u00e5nga piloter hanteras koden i Git, men datan ligger i en mapp som skrivits \u00f6ver flera g\u00e5nger och modellen sparas som en fil med ett datum i namnet. Det fungerar s\u00e5 l\u00e4nge samma person sitter kvar och minns hur det gick till. N\u00e4r modellen ska till produktion r\u00e4cker det inte. Ni beh\u00f6ver kunna \u00e5terskapa en modell exakt, j\u00e4mf\u00f6ra tv\u00e5 versioner och backa till en tidigare version om n\u00e5got g\u00e5r fel, vilket kr\u00e4ver att data, kod, konfiguration, beroenden och den f\u00e4rdiga modellen sparas och sp\u00e5ras tillsammans. Ett praktiskt f\u00f6rsta steg \u00e4r ett modellregister d\u00e4r varje tr\u00e4ningsk\u00f6rning kopplas till sitt dataset och sina parametrar. Lika viktigt \u00e4r milj\u00f6n: en modell tr\u00e4nad med en viss ramverks- och CUDA-version kan bete sig annorlunda i en annan. Containerbaserade milj\u00f6er som \u00e4r identiska i utveckling och produktion tar bort en stor del av den os\u00e4kerheten.<\/p>\n<p><strong>2. Ingen \u00f6vervakning efter drifts\u00e4ttning.<\/strong> I traditionell mjukvara m\u00e4rks det oftast n\u00e4r n\u00e5got g\u00e5r s\u00f6nder. En AI-modell kan d\u00e4remot forts\u00e4tta svara utan felmeddelanden och samtidigt leverera allt s\u00e4mre resultat. Det g\u00f6r \u00f6vervakning till en helt annan uppgift. Modell\u00f6vervakning beh\u00f6ver omfatta flera niv\u00e5er: svarstider, felfrekvens och resursanv\u00e4ndning p\u00e5 den tekniska niv\u00e5n, att indata ser ut som f\u00f6rv\u00e4ntat p\u00e5 dataniv\u00e5n, och att f\u00f6ruts\u00e4gelserna fortfarande h\u00e5ller kvalitet p\u00e5 modellniv\u00e5n. Det sista \u00e4r sv\u00e5rast, eftersom det korrekta svaret ofta inte \u00e4r k\u00e4nt f\u00f6rr\u00e4n l\u00e5ngt senare. Best\u00e4m d\u00e4rf\u00f6r i f\u00f6rv\u00e4g vilka m\u00e5tt som ska f\u00f6ljas, vilka tr\u00f6sklar som ska larma och hur \u00e5terkopplingen fr\u00e5n verksamheten samlas in.<\/p>\n<p><strong>3. Modell-drift som ingen uppt\u00e4cker.<\/strong> Modeller tr\u00e4nas p\u00e5 historisk data, men v\u00e4rlden st\u00e5r inte still. Kundbeteenden f\u00f6r\u00e4ndras, sensorer byts ut, nya produkter tillkommer och processer \u00e4ndras. Det leder till modell-drift: modellen presterar gradvis s\u00e4mre eftersom verkligheten inte l\u00e4ngre liknar tr\u00e4ningsdatan. Drift kan smyga sig p\u00e5 under m\u00e5nader utan att n\u00e5gon m\u00e4rker det, och det farliga \u00e4r inte att den uppst\u00e5r &#8211; det g\u00f6r den n\u00e4stan alltid &#8211; utan att den uppt\u00e4cks sent. D\u00e5 har felaktiga beslut redan fattats och f\u00f6rtroendet f\u00f6r AI-l\u00f6sningen hunnit skadas. L\u00f6sningen \u00e4r att behandla omtr\u00e4ning som en planerad del av livscykeln, inte som en n\u00f6d\u00e5tg\u00e4rd: f\u00f6lj f\u00f6rdelningen i indata och utdata \u00f6ver tid, larma vid avvikelser och ha en f\u00e4rdig process f\u00f6r att tr\u00e4na om, validera och drifts\u00e4tta en ny version.<\/p>\n<p><strong>4. Infrastruktur byggd f\u00f6r experiment.<\/strong> Under piloten har ofta ett team en GPU-server f\u00f6r sig sj\u00e4lv. N\u00e4r fler team och modeller tillkommer uppst\u00e5r konkurrens om resurserna. Utan gemensam schemal\u00e4ggning slutar det ofta med att GPU:er reserveras &#8220;f\u00f6r s\u00e4kerhets skull&#8221; och st\u00e5r oanv\u00e4nda, medan andra team v\u00e4ntar i k\u00f6. Vi ser regelbundet milj\u00f6er d\u00e4r dyr h\u00e5rdvara har l\u00e5g utnyttjandegrad samtidigt som utvecklarna upplever brist p\u00e5 kapacitet. En produktionsplattform beh\u00f6ver kunna f\u00f6rdela resurser mellan team och projekt, prioritera produktionsjobb framf\u00f6r experiment och ge en samlad bild av vad som k\u00f6rs var, och samtidigt m\u00f6ta de s\u00e4kerhetskrav som g\u00e4ller, vilket f\u00f6r m\u00e5nga inneb\u00e4r att data och modeller ska stanna i egen milj\u00f6.<\/p>\n<p><strong>5. Oklart \u00e4garskap.<\/strong> Den kanske mest underskattade f\u00e4llan \u00e4r organisatorisk. Datascientists bygger modellen, IT driftar infrastrukturen och verksamheten anv\u00e4nder resultatet. Men vem ansvarar f\u00f6r att modellen fungerar i produktion? Vem tar beslutet att tr\u00e4na om? Vem svarar n\u00e4r en f\u00f6ruts\u00e4gelse visar sig vara fel? Utan tydliga svar hamnar modellen i ett mellanl\u00e4ge d\u00e4r ingen riktigt \u00e4ger den. S\u00e4tt d\u00e4rf\u00f6r \u00e4garskapet innan modellen drifts\u00e4tts: varje modell i produktion b\u00f6r ha en namngiven \u00e4gare, definierade kvalitetsm\u00e5tt och en \u00f6verenskommen process f\u00f6r f\u00f6r\u00e4ndringar. Det l\u00e5ter byr\u00e5kratiskt, men \u00e4r i praktiken det som g\u00f6r att modellen \u00f6verlever n\u00e4r piloten \u00e4r slut.<\/p>\n<h2>S\u00e5 angriper AiQu problemen<\/h2>\n<p>Vi utvecklade AiQu, v\u00e5r egen MLOps-plattform, eftersom vi g\u00e5ng p\u00e5 g\u00e5ng s\u00e5g samma m\u00f6nster hos kunder: bra modeller och bra h\u00e5rdvara, men inget som band ihop dem i drift. AiQu ligger mellan infrastrukturen och de team som arbetar med AI och hj\u00e4lper till med flera av f\u00e4llorna ovan:<\/p>\n<ul>\n<li><strong>Identiska milj\u00f6er.<\/strong> Standardiserade, containerbaserade arbetsmilj\u00f6er g\u00f6r att utvecklingsmilj\u00f6n speglar produktionsmilj\u00f6n, vilket g\u00f6r resultat l\u00e4ttare att \u00e5terskapa.<\/li>\n<li><strong>Delad och styrd GPU-kapacitet.<\/strong> Schemal\u00e4ggning och resursstyrning f\u00f6rdelar GPU:erna mellan team och projekt, s\u00e5 att h\u00e5rdvaran anv\u00e4nds i st\u00e4llet f\u00f6r att st\u00e5 reserverad.<\/li>\n<li><strong>En samlad bild.<\/strong> Alla workloads och deras resursanv\u00e4ndning syns p\u00e5 ett st\u00e4lle, vilket g\u00f6r det l\u00e4ttare att \u00f6vervaka och prioritera.<\/li>\n<li><strong>Data som stannar hemma.<\/strong> AiQu kan k\u00f6ras helt i egen milj\u00f6 eller i ett svenskt datacenter, f\u00f6r organisationer med krav p\u00e5 datasuver\u00e4nitet.<\/li>\n<li><strong>Verktyg som h\u00e4nger ihop.<\/strong> I st\u00e4llet f\u00f6r att ers\u00e4tta etablerade verktyg f\u00f6r experimentsp\u00e5rning och modellregister knyter AiQu ihop dem i en sammanh\u00e5llen plattform.<\/li>\n<\/ul>\n<p>Vi vill samtidigt vara \u00e4rliga: ingen plattform l\u00f6ser f\u00e4lla 5. \u00c4garskap, processer och tydliga kvalitetsm\u00e5tt beh\u00f6ver organisationen sj\u00e4lv best\u00e4mma. Det vi kan g\u00f6ra \u00e4r att ge en grund d\u00e4r det blir enkelt att f\u00f6lja processerna, och att hj\u00e4lpa till att s\u00e4tta upp dem.<\/p>\n<h2>Ta n\u00e4sta steg mot produktion<\/h2>\n<p>Att skala AI handlar mindre om n\u00e4sta genombrott i modellerna och mer om att f\u00e5 grunderna p\u00e5 plats: sp\u00e5rbarhet, \u00f6vervakning, en plan f\u00f6r drift, en infrastruktur som t\u00e5l fler anv\u00e4ndare och ett tydligt \u00e4garskap. Den som l\u00f6ser det tidigt f\u00e5r inte bara fler modeller i produktion, utan ocks\u00e5 modeller som g\u00e5r att lita p\u00e5.<\/p>\n<div class=\"cta\">\n<p><strong>F\u00e5 v\u00e5r MLOps-checklista f\u00f6r produktion.<\/strong> Vi har sammanst\u00e4llt de fr\u00e5gor vi g\u00e5r igenom med v\u00e5ra kunder innan en modell g\u00e5r i drift, fr\u00e5n sp\u00e5rbarhet och \u00f6vervakning till \u00e4garskap och omtr\u00e4ning. Anv\u00e4nd den f\u00f6r att se var era AI-projekt st\u00e5r i dag, och vilka steg som \u00e5terst\u00e5r.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Most AI pilots never make it to production. Here are the five most common MLOps pitfalls\u2014traceability, monitoring, model operations, resource sharing, and ownership\u2014and what it takes to avoid them.<\/p>\n","protected":false},"author":15,"featured_media":122132,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[60],"tags":[],"class_list":["post-122534","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"Most AI pilots never make it to production. 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