{"id":25593,"date":"2024-05-10T09:39:05","date_gmt":"2024-05-10T09:39:05","guid":{"rendered":"https:\/\/auriane.teeptrak.com\/?page_id=25593"},"modified":"2026-06-14T05:45:47","modified_gmt":"2026-06-14T05:45:47","slug":"machine-learning","status":"publish","type":"page","link":"https:\/\/teeptrak.com\/fr\/machine-learning\/","title":{"rendered":"Machine Learning"},"content":{"rendered":"<p>[et_pb_section fb_built=\u00a0\u00bb1&Prime; admin_label=\u00a0\u00bbTemplate B\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.4&Prime; background_color=\u00a0\u00bb#FFFFFF\u00a0\u00bb custom_padding=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_row _builder_version=\u00a0\u00bb4.27.4&Prime; width=\u00a0\u00bb100%\u00a0\u00bb max_width=\u00a0\u00bb100%\u00a0\u00bb custom_padding=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_column 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details{background:#fff;border:1px solid var(--line);border-radius:14px;padding:2px 22px;margin:12px 0;}\n.mlk summary{cursor:pointer;font-weight:750;font-size:16.5px;padding:18px 0;list-style:none;display:flex;justify-content:space-between;align-items:center;}\n.mlk summary::-webkit-details-marker{display:none;}\n.mlk summary:after{content:'+';color:var(--red);font-size:24px;font-weight:600;}\n.mlk details[open] summary:after{content:'\\2013';}\n.mlk details p{color:var(--muted);line-height:1.65;margin:0 0 18px;font-size:15px;}\n.mlk .src{display:flex;flex-wrap:wrap;gap:10px;justify-content:center;margin-top:34px;}\n.mlk .chip{border:1px solid var(--line);background:#fff;border-radius:100px;padding:9px 18px;font-weight:750;font-size:14px;color:var(--ink);box-shadow:0 2px 8px rgba(0,0,0,.03);}\n.mlk .final{background:linear-gradient(155deg,#201e1c,#2d2723);color:#fff;text-align:center;}\n.mlk .final h2{color:#fff;}\n@media(prefers-reduced-motion:reduce){.mlk *{animation:none!important;}.mlk .rise{opacity:1;transform:none;}.mlk .draw,.mlk .drawp{stroke-dashoffset:0;}.mlk .flag{opacity:1;}}\n\/*ttkfix*\/.mlk .hero h1{color:#fff!important}.mlk h1 .hl{color:var(--accent)!important}.mlk h2{color:#232120!important}.mlk .final h2{color:#fff!important}.mlk h3{color:#232120!important}.mlk h4{color:#232120!important}.mlk .klab,.mlk .cch{color:#6f6b68!important}.mlk .atime{color:#6f6b68!important}<\/style>\n<div class=\"mlk\">\n<div class=\"hero\">\n<div class=\"w heroGrid\">\n<div>\n  <span class=\"eyebrow rise\">Machine Learning \u00b7 Pr\u00e9dictif<\/span><\/p>\n<h1 class=\"rise d1\">Voyez la perte <span class=\"hl\">arriver.<\/span><\/h1>\n<pee class=\"lead rise d2\">La couche Machine Learning de TeepTrak apprend ce qu&rsquo;est la \u00ab normale \u00bb sur chaque ligne, puis signale d\u00e9rives, micro-arr\u00eats naissants et probl\u00e8mes qualit\u00e9 \u00e9mergents avant qu&rsquo;ils ne vous co\u00fbtent la moindre pi\u00e8ce \u2014 avec la cause probable d\u00e9j\u00e0 associ\u00e9e.<\/pee>\n<ul class=\"ul rise d3\">\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Fonctionne sur les donn\u00e9es TeepTrak que vous collectez d\u00e9j\u00e0<\/li>\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Alerte pr\u00e9coce \u2014 des minutes \u00e0 des heures avant le d\u00e9passement<\/li>\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Explicable \u2014 chaque alerte montre le pourquoi, pas seulement le quoi<\/li>\n<\/ul>\n<div class=\"cta rise d4\"><a class=\"btn btn-red\" href=\"https:\/\/teeptrak.com\/en\/demonstration\/\">R\u00e9server une d\u00e9mo<\/a><a class=\"btn btn-ghost\" href=\"#how\">Voir comment \u00e7a marche<\/a><\/div>\n<\/p><\/div>\n<div class=\"rise d2\">\n<div class=\"dash\">\n<div class=\"dbar\"><img decoding=\"async\" class=\"dlogo\" src=\"https:\/\/teeptrak.com\/wp-content\/uploads\/2023\/05\/TeepTrak_All-Logos_TeepTrak_Logo-.svg\" alt=\"TeepTrak\"\/><span class=\"dctx\">D\u00e9tection d&rsquo;anomalies \u2014 Four B2<\/span><span class=\"dlive\"><i><\/i>ALERTE<\/span><\/div>\n<div class=\"dbody\">\n<div class=\"card\">\n<div class=\"cch\"><span>Pression \u00b7 r\u00e9el vs normale apprise<\/span><span style=\"color:var(--red)\">\u26a0 d\u00e9passement dans ~25 min<\/span><\/div>\n<p>     <svg width=\"100%\" height=\"150\" viewBox=\"0 0 330 150\" preserveAspectRatio=\"none\">\n      <rect x=\"0\" y=\"60\" width=\"330\" height=\"40\" fill=\"#eafaf0\"\/>\n      <text x=\"6\" y=\"74\" fill=\"#16A34A\" font-size=\"9\" font-family=\"Arial\" font-weight=\"700\">normale apprise<\/text>\n      <line x1=\"0\" y1=\"32\" x2=\"330\" y2=\"32\" stroke=\"#EB352C\" stroke-width=\"1.4\" stroke-dasharray=\"5 4\"\/>\n      <text x=\"324\" y=\"28\" fill=\"#EB352C\" font-size=\"9\" font-family=\"Arial\" font-weight=\"800\" text-anchor=\"end\">limite haute<\/text>\n      <polyline class=\"draw\" points=\"6,92 46,88 86,90 126,82 166,84 206,74 240,66\" fill=\"none\" stroke=\"#232120\" stroke-width=\"2.6\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n      <polyline class=\"drawp\" points=\"240,66 276,54 312,38\" fill=\"none\" stroke=\"#FF674C\" stroke-width=\"2.6\" stroke-dasharray=\"5 5\" stroke-linecap=\"round\"\/>\n      <g class=\"flag\"><circle class=\"flagdot\" cx=\"240\" cy=\"66\" r=\"6\" fill=\"#EB352C\"\/><circle cx=\"240\" cy=\"66\" r=\"11\" fill=\"none\" stroke=\"#EB352C\" stroke-width=\"1.5\" opacity=\".5\"\/><\/g>\n     <\/svg>\n    <\/div>\n<div class=\"kpis\">\n<div class=\"kpi kl\">\n<div class=\"klab\">Anomalies \u00b7 7 j<\/div>\n<div class=\"kval\">14<\/div>\n<div class=\"kd up\">d\u00e9tect\u00e9es t\u00f4t<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">D\u00e9lai moyen<\/div>\n<div class=\"kval\">23 min<\/div>\n<div class=\"kd up\">avant d\u00e9passement<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">Confiance<\/div>\n<div class=\"kval\">94%<\/div>\n<div class=\"kd up\">\u25b2 affin\u00e9e<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">Bruit d&rsquo;alerte<\/div>\n<div class=\"kval\">\u221280%<\/div>\n<div class=\"kd up\">vs seuils<\/div>\n<\/div><\/div>\n<div class=\"card\">\n<div class=\"cch\"><span>Alertes r\u00e9centes<\/span><span>cause probable<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#EB352C\"><\/span><span class=\"aname\">D\u00e9rive de pression<\/span><span class=\"asub\">\u00b7 Oven B2 \u00b7 valve wear<\/span><span class=\"atime\">11:20<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#F59E0B\"><\/span><span class=\"aname\">D\u00e9rive de cycle<\/span><span class=\"asub\">\u00b7 Line 4 \u00b7 tooling<\/span><span class=\"atime\">10:48<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#16A34A\"><\/span><span class=\"aname\">R\u00e9solu<\/span><span class=\"asub\">\u00b7 Temp \u00b7 Dryer 2<\/span><span class=\"atime\">09:55<\/span><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n<div class=\"trust\">\n<div class=\"w\"><b>450+ factories<\/b> \u00b7 30+ countries \u00b7 <b>4.7\/5<\/b> on G2 &amp; Capterra<\/div>\n<\/div>\n<section>\n<div class=\"w\">\n<div class=\"kick rise\">Pourquoi le Machine Learning<\/div>\n<h2 class=\"rise d1\">Le signal est d\u00e9j\u00e0 dans vos donn\u00e9es.<\/h2>\n<pee class=\"sub rise d2\">Vous collectez des milliers de points de donn\u00e9es par poste. Le motif qui pr\u00e9dit le rebut de ce soir s&rsquo;y trouve \u2014 le Machine Learning le rep\u00e8re avant que vous ne le ressentiez.<\/pee>\n<div class=\"why\">\n<div class=\"wc rise d1\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M3 17c3-9 6-9 9 0s6 9 9 0\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\"\/><\/svg><\/div>\n<h3>Apprend votre normale<\/h3>\n<pee>Une r\u00e9f\u00e9rence vivante par ligne et par produit, construite \u00e0 partir des temps de cycle, arr\u00eats, param\u00e8tres et qualit\u00e9. Aucune ligne n&rsquo;est jug\u00e9e selon la m\u00eame r\u00e8gle g\u00e9n\u00e9rique.<\/pee><\/div>\n<div class=\"wc rise d2\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M3 14l5-5 4 3 8-9\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><circle cx=\"20\" cy=\"4\" r=\"2.4\" fill=\"#fff\"\/><\/svg><\/div>\n<h3>Pr\u00e9dit l&rsquo;anormal<\/h3>\n<pee>Les mod\u00e8les de tendance et de motif l\u00e8vent une alerte tant qu&rsquo;il est encore temps de corriger \u2014 des minutes \u00e0 des heures d&rsquo;avance, pas une autopsie le mois suivant.<\/pee><\/div>\n<div class=\"wc rise d3\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><circle cx=\"11\" cy=\"11\" r=\"7\" stroke=\"#fff\" stroke-width=\"2.2\"\/><path d=\"M16 16l5 5\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\"\/><\/svg><\/div>\n<h3>S&rsquo;explique<\/h3>\n<pee>Chaque alerte est livr\u00e9e avec les facteurs contributifs et l&rsquo;endroit o\u00f9 regarder en premier. Une information \u00e0 laquelle l&rsquo;atelier se fie \u2014 pas une bo\u00eete noire qui crie au loup.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"screens\">\n<div class=\"w\">\n<div class=\"kick rise\">Au c\u0153ur du mod\u00e8le<\/div>\n<h2 class=\"rise d1\">Des signaux bruts \u00e0 une alerte exploitable.<\/h2>\n<pee class=\"sub rise d2\">Quatre fa\u00e7ons dont le mod\u00e8le transforme vos donn\u00e9es existantes en anticipation.<\/pee>\n<div class=\"sgrid\">\n<div class=\"scard rise d1\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">D\u00e9tection de d\u00e9rive<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><rect x=\"0\" y=\"28\" width=\"300\" height=\"20\" fill=\"#eafaf0\"\/><line x1=\"0\" y1=\"14\" x2=\"300\" y2=\"14\" stroke=\"#EB352C\" stroke-width=\"1.3\" stroke-dasharray=\"4 4\"\/><polyline class=\"draw\" points=\"4,44 60,42 116,38 172,30 228,22 286,14\" fill=\"none\" stroke=\"#232120\" stroke-width=\"2.4\" stroke-linecap=\"round\"\/><circle class=\"flagdot\" cx=\"228\" cy=\"22\" r=\"5\" fill=\"#EB352C\"\/><\/svg><\/div>\n<h4>Capte la d\u00e9rive lente<\/h4>\n<pee>La d\u00e9rive douce du vendredi apr\u00e8s-midi qu&rsquo;aucun seuil ne d\u00e9clenche \u2014 signal\u00e9e d\u00e8s que la tendance s&rsquo;inverse.<\/pee><\/div>\n<div class=\"scard rise d2\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Regroupement de motifs<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><g fill=\"#cfccc9\"><circle cx=\"40\" cy=\"24\" r=\"4\"\/><circle cx=\"55\" cy=\"34\" r=\"4\"\/><circle cx=\"48\" cy=\"46\" r=\"4\"\/><circle cx=\"66\" cy=\"40\" r=\"4\"\/><\/g><g fill=\"#FF674C\"><circle cx=\"150\" cy=\"20\" r=\"4\"\/><circle cx=\"166\" cy=\"30\" r=\"4\"\/><circle cx=\"158\" cy=\"42\" r=\"4\"\/><\/g><g fill=\"#EB352C\"><circle cx=\"250\" cy=\"38\" r=\"5\"\/><circle cx=\"264\" cy=\"48\" r=\"5\"\/><circle cx=\"240\" cy=\"50\" r=\"5\"\/><\/g><ellipse cx=\"252\" cy=\"46\" rx=\"26\" ry=\"18\" fill=\"none\" stroke=\"#EB352C\" stroke-width=\"1.4\" stroke-dasharray=\"3 3\"\/><\/svg><\/div>\n<h4>Trouve le d\u00e9faut r\u00e9current<\/h4>\n<pee>Regroupe les \u00e9v\u00e9nements similaires pour faire \u00e9merger une cause racine r\u00e9currente \u2014 m\u00eame entre postes et produits.<\/pee><\/div>\n<div class=\"scard rise d3\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Alerte explicable<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\" font-family=\"Arial\"><g><rect x=\"60\" y=\"8\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"8\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#EB352C\" style=\"--p:.9\"\/><text x=\"0\" y=\"18\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">valve<\/text><rect x=\"60\" y=\"28\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"28\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#FF674C\" style=\"--p:.6\"\/><text x=\"0\" y=\"38\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">ambient<\/text><rect x=\"60\" y=\"48\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"48\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#cfccc9\" style=\"--p:.3\"\/><text x=\"0\" y=\"58\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">recipe<\/text><\/g><\/svg><\/div>\n<h4>Montre le pourquoi<\/h4>\n<pee>Chaque alerte hi\u00e9rarchise les facteurs en cause, pour que l&rsquo;\u00e9quipe parte de la cause la plus probable \u2014 pas de z\u00e9ro.<\/pee><\/div>\n<div class=\"scard rise d4\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Chronologie des anomalies<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><line x1=\"0\" y1=\"50\" x2=\"300\" y2=\"50\" stroke=\"#e7e6e5\" stroke-width=\"2\"\/><g><circle cx=\"50\" cy=\"50\" r=\"5\" fill=\"#16A34A\"\/><circle cx=\"120\" cy=\"50\" r=\"5\" fill=\"#F59E0B\"\/><circle cx=\"190\" cy=\"50\" r=\"5\" fill=\"#16A34A\"\/><circle class=\"flagdot\" cx=\"262\" cy=\"50\" r=\"6\" fill=\"#EB352C\"\/><\/g><rect class=\"bar\" x=\"256\" y=\"14\" width=\"12\" height=\"26\" rx=\"3\" fill=\"#EB352C\" style=\"animation-delay:.5s;transform-origin:bottom\"\/><\/svg><\/div>\n<h4>Une tra\u00e7abilit\u00e9 claire<\/h4>\n<pee>Chaque anomalie, journalis\u00e9e avec son contexte \u2014 \u00e0 int\u00e9grer \u00e0 vos rituels d&rsquo;am\u00e9lioration continue, votre BI ou votre GMAO.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section id=\"how\">\n<div class=\"w\">\n<div class=\"kick rise\">How it works<\/div>\n<h2 class=\"rise d1\">Aucun projet data. Il fonctionne sur ce que vous avez d\u00e9j\u00e0.<\/h2>\n<div class=\"steps\">\n<div class=\"step rise d1\">\n<div class=\"n\">1<\/div>\n<h3>Alimentez-le avec vos donn\u00e9es<\/h3>\n<pee>TRS, arr\u00eats, temps de cycle, param\u00e8tres de process et qualit\u00e9 \u2014 d\u00e9j\u00e0 collect\u00e9s par TeepTrak. Rien de nouveau \u00e0 installer, aucun capteur suppl\u00e9mentaire.<\/pee><\/div>\n<div class=\"step rise d2\">\n<div class=\"n\">2<\/div>\n<h3>Il apprend la normale<\/h3>\n<pee>Le mod\u00e8le construit une r\u00e9f\u00e9rence pour chaque ligne et produit et l&rsquo;affine \u00e0 mesure que les conditions \u00e9voluent. Votre usine, pas un manuel.<\/pee><\/div>\n<div class=\"step rise d3\">\n<div class=\"n\">3<\/div>\n<h3>Il alerte t\u00f4t<\/h3>\n<pee>Les pr\u00e9dictions arrivent l\u00e0 o\u00f9 votre \u00e9quipe travaille d\u00e9j\u00e0 \u2014 tableau de bord, e-mail, smartphone ou API \u2014 chacune avec la cause probable associ\u00e9e.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"stats\">\n<div class=\"w\">\n<div class=\"srow\">\n<div class=\"rise d1\">\n<div class=\"v\">23 min<\/div>\n<div class=\"l\">d&rsquo;alerte moyenne avant d\u00e9passement<\/div>\n<\/div>\n<div class=\"rise d2\">\n<div class=\"v\">\u221280%<\/div>\n<div class=\"l\">de bruit d&rsquo;alerte vs seuils fixes<\/div>\n<\/div>\n<div class=\"rise d3\">\n<div class=\"v\">0<\/div>\n<div class=\"l\">capteur suppl\u00e9mentaire requis<\/div>\n<\/div>\n<div class=\"rise d4\">\n<div class=\"v\">94%<\/div>\n<div class=\"l\">de confiance, et en hausse<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<section>\n<div class=\"w\">\n<div class=\"kick rise\">Tourne sur votre stack<\/div>\n<h2 class=\"rise d1\">B\u00e2ti sur les donn\u00e9es que TeepTrak collecte d\u00e9j\u00e0.<\/h2>\n<div class=\"src rise d2\"><span class=\"chip\">PerfTrak<\/span><span class=\"chip\">QualTrak<\/span><span class=\"chip\">ProcessTrak<\/span><span class=\"chip\">OPC UA<\/span><span class=\"chip\">MES<\/span><span class=\"chip\">BI \/ data lake<\/span><span class=\"chip\">REST API<\/span><\/div>\n<div class=\"faq\">\n<details class=\"rise\">\n<summary>Quelle quantit\u00e9 de donn\u00e9es faut-il ?<\/summary>\n<pee>Si vous utilisez d\u00e9j\u00e0 TeepTrak, vous en avez assez. Le mod\u00e8le s&rsquo;entra\u00eene sur l&rsquo;historique collect\u00e9 et fait \u00e9merger des motifs en quelques jours \u2014 puis s&rsquo;am\u00e9liore au fil du temps.<\/pee><\/details>\n<details class=\"rise\">\n<summary>Est-ce une bo\u00eete noire ?<\/summary>\n<pee>Non \u2014 c&rsquo;est tout l&rsquo;int\u00e9r\u00eat. Chaque alerte hi\u00e9rarchise les facteurs d\u00e9clencheurs et indique o\u00f9 regarder en premier ; votre \u00e9quipe agit sur une information v\u00e9rifiable, pas sur un score myst\u00e9rieux.<\/pee><\/details>\n<details class=\"rise\">\n<summary>O\u00f9 apparaissent les pr\u00e9dictions ?<\/summary>\n<pee>L\u00e0 o\u00f9 votre \u00e9quipe travaille d\u00e9j\u00e0 : tableaux de bord TeepTrak, alertes smartphone, e-mail, ou pouss\u00e9es vers votre BI, votre MES ou votre GMAO via API.<\/pee><\/details>\n<details class=\"rise\">\n<summary>Faut-il un data scientist ?<\/summary>\n<pee>Non. TeepTrak configure et affine les mod\u00e8les avec vous. Vous recevez les alertes pr\u00e9coces et les explications ; nous g\u00e9rons le machine learning.<\/pee><\/details>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"final\">\n<div class=\"w\">\n<h2 class=\"rise\">Agissez avant que la perte ne survienne.<\/h2>\n<pee class=\"sub rise d1\" style=\"color:#cfccc9\">Une session de travail de 30 minutes avec un ing\u00e9nieur TeepTrak \u2014 nous examinons vos propres donn\u00e9es et montrons o\u00f9 le mod\u00e8le vous aurait alert\u00e9 en premier.<\/pee>\n<div class=\"cta rise d2\" style=\"justify-content:center;margin-top:28px\"><a class=\"btn btn-red\" href=\"https:\/\/teeptrak.com\/en\/demonstration\/\">R\u00e9server ma d\u00e9mo<\/a><a class=\"btn btn-ghost\" href=\"https:\/\/teeptrak.com\/en\/solutions\/\">Toutes les solutions<\/a><\/div>\n<\/div>\n<\/section>\n<\/div>\n<p>[\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Exploitez vos donn\u00e9es de production avec l\u2019IA TeepTrak. Anticipez les pannes, am\u00e9liorez la performance et optimisez votre TRS avec le machine learning.<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-25593","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning<\/title>\n<meta name=\"description\" content=\"Utilisez l\u2019IA pour d\u00e9tecter anomalies &amp; anticiper les pannes. 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