{"id":1088,"date":"2026-04-30T14:22:51","date_gmt":"2026-04-30T14:22:51","guid":{"rendered":"https:\/\/pythonia.fr\/?page_id=1088"},"modified":"2026-04-30T14:27:48","modified_gmt":"2026-04-30T14:27:48","slug":"elementor-1088","status":"publish","type":"page","link":"https:\/\/pythonia.fr\/index.php\/elementor-1088\/","title":{"rendered":"Elementor #1088"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1088\" class=\"elementor elementor-1088\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d0e2e08 e-flex e-con-boxed e-con e-parent\" data-id=\"d0e2e08\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bbd0a56 elementor-widget elementor-widget-heading\" data-id=\"bbd0a56\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><strong>D\u00e9ployer une application IA (ML \/ NLP) en production<\/strong>.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ae11ac4 e-grid e-con-boxed e-con e-parent\" data-id=\"ae11ac4\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-157c515 elementor-widget elementor-widget-heading\" data-id=\"157c515\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap;font-family:sans-serif\">\n  <!-- Colonne gauche : Objectifs -->\n  <div style=\"flex:2;min-width:300px\">\n    <h3 style=\"color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px;font-size:16px\">\n      Objectifs :\n    <\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:2;margin-left:20px\">\n      <li>Packager un mod\u00e8le ML et concevoir une API d'inf\u00e9rence robuste avec FastAPI et Docker<\/li>\n      <li>Choisir et mettre en \u0153uvre la bonne strat\u00e9gie de d\u00e9ploiement (CPU, GPU, serverless, batch)<\/li>\n      <li>Exposer une application LLM en production avec streaming, retry et cache s\u00e9mantique<\/li>\n      <li>Mettre en place un monitoring adapt\u00e9 \u00e0 l'IA et d\u00e9tecter le drift de donn\u00e9es et de mod\u00e8le<\/li>\n      <li>Industrialiser avec un pipeline CI\/CD sp\u00e9cifique IA et appliquer les exigences RGPD et IA Act<\/li>\n    <\/ul>\n  <\/div>\n  <!-- Colonne droite : Tarif + liens -->\n  <div style=\"flex:1;min-width:200px;background:#0B1D33;padding:25px;border-radius:12px;height:fit-content\">\n    <div style=\"padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      <span style=\"color:#8899AA;font-size:12px;text-transform:uppercase;letter-spacing:0.5px\">Tarif inter \/ participant<\/span>\n      <div style=\"color:#FFFFFF;font-size:22px;font-weight:bold;margin-top:4px\">\n        2 500 \u20ac <span style=\"font-size:13px;font-weight:normal;color:#8899AA\">HT<\/span>\n      <\/div>\n    <\/div>\n    <a href=\"http:\/\/pythonia.fr\/wp-content\/uploads\/2026\/04\/Programme_Cloud_4_Deploiement_IA_Pythonia.pdf\" target=\"_blank\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc4 Programme (PDF)\n    <\/a>\n    <a href=\"#\" id=\"btn-dates\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc5 Voir les dates\n    <\/a>\n    <button id=\"btn-contact\" style=\"display:block;width:100%;margin-top:20px;padding:15px 20px;background:#5DADE2;color:#FFFFFF;font-size:15px;font-weight:bold;border:none;border-radius:8px;cursor:pointer\">\n      \u2709\ufe0f Demande d'information\n    <\/button>\n  <\/div>\n<\/div><\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b7a68a9 e-grid e-con-boxed e-con e-parent\" data-id=\"b7a68a9\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9b99ae1 elementor-widget elementor-widget-heading\" data-id=\"9b99ae1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap\">\n\n  <!-- Colonne gauche : Contenu -->\n  <div style=\"flex:2;min-width:300px\">\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Public vis\u00e9<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Data scientists souhaitant passer du notebook exp\u00e9rimental \u00e0 la production<\/li>\n      <li>ML engineers charg\u00e9s de mettre en production des mod\u00e8les ML ou des applications LLM<\/li>\n      <li>D\u00e9veloppeurs Python avanc\u00e9s intervenant sur des projets IA en entreprise<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Objectifs p\u00e9dagogiques<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Comprendre les enjeux sp\u00e9cifiques du d\u00e9ploiement d'applications IA<\/li>\n      <li>Packager un mod\u00e8le ML : s\u00e9rialisation, versioning, reproductibilit\u00e9<\/li>\n      <li>Concevoir une API d'inf\u00e9rence robuste (FastAPI + mod\u00e8le)<\/li>\n      <li>Conteneuriser une application IA avec Docker et optimiser les images<\/li>\n      <li>Choisir une strat\u00e9gie de d\u00e9ploiement adapt\u00e9e (CPU, GPU, serverless, batch)<\/li>\n      <li>D\u00e9ployer sur Azure ML, AWS SageMaker ou solutions plus l\u00e9g\u00e8res<\/li>\n      <li>Exposer une API int\u00e9grant un LLM externe avec streaming et gestion des erreurs<\/li>\n      <li>Mettre en place un monitoring adapt\u00e9 aux applications IA<\/li>\n      <li>D\u00e9tecter le drift de donn\u00e9es et le drift de mod\u00e8le<\/li>\n      <li>Ma\u00eetriser les co\u00fbts d'inf\u00e9rence : batching, caching, mod\u00e8les hybrides<\/li>\n      <li>Mettre en place un pipeline CI\/CD pour mod\u00e8les : test, validation, d\u00e9ploiement<\/li>\n      <li>Appliquer les bonnes pratiques de s\u00e9curit\u00e9 (RGPD, PII, prompt injection)<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Pr\u00e9requis<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Python avanc\u00e9 : POO, typage, gestion d'exceptions, packaging<\/li>\n      <li>Entra\u00eenement d'au moins un mod\u00e8le avec scikit-learn, PyTorch ou HuggingFace<\/li>\n      <li>Notions de cloud : avoir d\u00e9j\u00e0 d\u00e9ploy\u00e9 une application (n'importe laquelle)<\/li>\n      <li>Notions de Docker (un rappel est propos\u00e9 en d\u00e9but de formation)<\/li>\n      <li>Aisance avec Git, CLI, SSH<\/li>\n      <li>Compte GitHub personnel et carte bancaire pour les providers cloud (cr\u00e9dits fournis)<\/li>\n    <\/ul>\n\n    <h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">M\u00e9thodes p\u00e9dagogiques<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Alternance de th\u00e9orie (35%) et de pratique (65%)<\/li>\n      <li>Live coding : construction progressive d'une application IA d\u00e9ploy\u00e9e de bout en bout<\/li>\n      <li>P\u00e9dagogie par la comparaison : m\u00eames probl\u00e8mes r\u00e9solus avec plusieurs plateformes<\/li>\n      <li>Analyse d'incidents : \u00e9tude de cas r\u00e9els d'\u00e9checs de mise en production IA<\/li>\n      <li>Retours personnalis\u00e9s : revue individuelle de chaque projet final<\/li>\n      <li>Code source, templates MLOps et checklists de production remis aux stagiaires (acc\u00e8s p\u00e9renne)<\/li>\n    <\/ul>\n\n    <h1 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:40px;margin-bottom:20px\">Programme d\u00e9taill\u00e9<\/h1>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 1 \u2014 Fondamentaux du MLOps et packaging de mod\u00e8les<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Diff\u00e9rences entre une app classique et une app IA en production<\/li>\n      <li>Pourquoi 80 % des mod\u00e8les n'atteignent jamais la production<\/li>\n      <li>Principes du MLOps : reproductibilit\u00e9, automatisation, monitoring<\/li>\n      <li>Cycle de vie d'un mod\u00e8le : donn\u00e9es, training, validation, d\u00e9ploiement, monitoring<\/li>\n      <li>R\u00f4le des diff\u00e9rentes \u00e9quipes : data scientist, ML engineer, DevOps<\/li>\n      <li>Panorama des plateformes MLOps : MLflow, DVC, Kubeflow, ZenML, Metaflow<\/li>\n      <li>Cartographie des solutions cloud : Azure ML, AWS SageMaker, GCP Vertex AI<\/li>\n      <li>S\u00e9rialisation des mod\u00e8les : pickle, joblib, ONNX, safetensors<\/li>\n      <li>Versioning des mod\u00e8les : MLflow Model Registry, DVC<\/li>\n      <li>Versioning des donn\u00e9es : pourquoi, comment (DVC, Git LFS)<\/li>\n      <li>Reproductibilit\u00e9 : pinning des versions, environnements isol\u00e9s<\/li>\n      <li>Construire une API d'inf\u00e9rence avec FastAPI : lifespan events, validation Pydantic<\/li>\n      <li>Gestion des erreurs sp\u00e9cifiques \u00e0 l'IA : OOM, timeout, valeurs aberrantes<\/li>\n      <li>Tests de l'API d'inf\u00e9rence : cas nominaux, cas d\u00e9grad\u00e9s<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Packager un mod\u00e8le scikit-learn avec versioning MLflow<br>\n    \u2192 TP2 : Construire une API FastAPI d'inf\u00e9rence avec validation stricte<br>\n    \u2192 TP3 : \u00c9crire les tests de l'API couvrant cas nominaux et cas limites<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 2 \u2014 Docker et strat\u00e9gies de d\u00e9ploiement IA<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Rappels Docker : images, conteneurs, Dockerfile, docker-compose<\/li>\n      <li>Sp\u00e9cificit\u00e9s des images IA : taille, GPU, d\u00e9pendances lourdes<\/li>\n      <li>Multi-stage builds : r\u00e9duire drastiquement la taille des images<\/li>\n      <li>Choix de l'image de base : python:slim, distroless, nvidia\/cuda<\/li>\n      <li>Gestion des mod\u00e8les dans les images : embarquer ou t\u00e9l\u00e9charger au d\u00e9marrage<\/li>\n      <li>Optimisation : couches, cache, .dockerignore<\/li>\n      <li>Tester une image en local avant d\u00e9ploiement<\/li>\n      <li>Publication sur un registry : Docker Hub, Azure Container Registry, ECR<\/li>\n      <li>Panorama des options : conteneur simple, serverless, managed ML platform<\/li>\n      <li>Inf\u00e9rence synchrone vs asynchrone : crit\u00e8res de choix<\/li>\n      <li>Inf\u00e9rence batch : cas d'usage et outillage<\/li>\n      <li>D\u00e9ploiement CPU vs GPU : quand, combien \u00e7a co\u00fbte<\/li>\n      <li>Azure ML : endpoints manag\u00e9s, principe et guide pas \u00e0 pas<\/li>\n      <li>AWS SageMaker : endpoints, serverless inference<\/li>\n      <li>Solutions plus l\u00e9g\u00e8res : Modal, Replicate, RunPod pour les GPU<\/li>\n      <li>Kubernetes pour l'IA : KServe, BentoML (survol pour culture)<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Conteneuriser l'API d'inf\u00e9rence du jour 1 et optimiser la taille<br>\n    \u2192 TP2 : D\u00e9ployer le conteneur sur Render ou Railway, tester \u00e0 distance<br>\n    \u2192 TP3 : D\u00e9ployer le m\u00eame mod\u00e8le sur Azure ML et comparer les approches<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 3 \u2014 Applications LLM et monitoring en production<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Architecture d'une application LLM : clients, proxy, observabilit\u00e9<\/li>\n      <li>Proxy LLM : LiteLLM, pourquoi et quand l'utiliser<\/li>\n      <li>Gestion des cl\u00e9s API et rotation des secrets<\/li>\n      <li>Retry, backoff, timeouts : patterns pour r\u00e9sister aux pannes des providers<\/li>\n      <li>Streaming des r\u00e9ponses LLM : SSE, WebSocket, FastAPI<\/li>\n      <li>Gestion des co\u00fbts par requ\u00eate : tokenisation, comptage, attribution<\/li>\n      <li>Cache s\u00e9mantique : principes et impl\u00e9mentation avec Redis<\/li>\n      <li>Gestion des conversations et du contexte long<\/li>\n      <li>Logs structur\u00e9s pour l'IA : requ\u00eate, mod\u00e8le, latence, co\u00fbt, confiance<\/li>\n      <li>M\u00e9triques essentielles : latence, erreurs, volume, co\u00fbts, performances mod\u00e8le<\/li>\n      <li>Outils : Langfuse, Arize, Fiddler, Evidently, Prometheus + Grafana<\/li>\n      <li>Observabilit\u00e9 sp\u00e9cifique LLM : traces, \u00e9valuations, feedback utilisateur<\/li>\n      <li>D\u00e9tection de drift des donn\u00e9es d'entr\u00e9e et du mod\u00e8le<\/li>\n      <li>Feedback loop utilisateur : boutons thumbs up\/down, analyses<\/li>\n      <li>Alerting intelligent : seuils pertinents, \u00e9viter la fatigue d'alertes<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques :<\/strong><br>\n    \u2192 TP1 : Transformer l'API d'inf\u00e9rence en API LLM avec streaming et retry<br>\n    \u2192 TP2 : Mettre en place un monitoring complet avec Langfuse<br>\n    \u2192 TP3 : Simuler un drift de donn\u00e9es et v\u00e9rifier sa d\u00e9tection par le monitoring<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">JOUR 4 \u2014 CI\/CD, s\u00e9curit\u00e9 et projet final<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Pipeline CI\/CD sp\u00e9cifique \u00e0 l'IA avec GitHub Actions<\/li>\n      <li>Tests automatis\u00e9s : code, API, mod\u00e8le (performance, non-r\u00e9gression)<\/li>\n      <li>\u00c9tapes cl\u00e9s : lint, tests, build image, scan s\u00e9curit\u00e9, d\u00e9ploiement<\/li>\n      <li>D\u00e9ploiements progressifs : blue-green, canary, shadow<\/li>\n      <li>Rollback rapide en cas d'incident<\/li>\n      <li>Feature flags pour activer \/ d\u00e9sactiver un mod\u00e8le<\/li>\n      <li>Optimisation des co\u00fbts d'inf\u00e9rence : batching, caching, mod\u00e8les hybrides<\/li>\n      <li>Mod\u00e8le en cascade : petit mod\u00e8le rapide puis grand mod\u00e8le si n\u00e9cessaire<\/li>\n      <li>Quantization et distillation : quand et comment (survol)<\/li>\n      <li>S\u00e9curit\u00e9 des applications IA : panorama des risques<\/li>\n      <li>Protection des mod\u00e8les : extraction, inversion, adversarial<\/li>\n      <li>Gestion des PII : d\u00e9tection, anonymisation, conformit\u00e9 RGPD<\/li>\n      <li>Prompt injection et jailbreak : d\u00e9tection et mitigation<\/li>\n      <li>Guardrails : Llama Guard, NeMo Guardrails, approches custom<\/li>\n      <li>Conformit\u00e9 : AI Act europ\u00e9en, obligations selon le niveau de risque<\/li>\n      <li>Documentation du mod\u00e8le : model card, data card, log d'usage<\/li>\n      <li>Gouvernance : validation, audit, tra\u00e7abilit\u00e9<\/li>\n    <\/ul>\n    <p style=\"font-size:14px;color:#FFFFFF;margin-left:20px;margin-top:15px\"><strong>\u25a0 Travaux pratiques \u2014 Projet final :<\/strong><br>\n    \u2192 Chaque participant finalise une application IA compl\u00e8te et d\u00e9ploy\u00e9e<br>\n    \u2192 Mod\u00e8le ML ou int\u00e9gration LLM, API conteneuris\u00e9e, pipeline CI\/CD actif, monitoring configur\u00e9<br>\n    \u2192 Soutenance individuelle de 20 min : architecture, choix, d\u00e9monstration, limites<\/p>\n\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">\u00c9valuation<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Test de positionnement en d\u00e9but de formation<\/li>\n      <li>\u00c9valuations formatives : exercices pratiques, d\u00e9bogage collectif, revues d'architecture<\/li>\n      <li>QCM mi-parcours de 25 questions (Jour 2)<\/li>\n      <li>QCM final de 35 questions (Jour 4) \u2014 crit\u00e8re de r\u00e9ussite : 60%<\/li>\n      <li>Soutenance individuelle du projet final (20 min) avec d\u00e9monstration live<\/li>\n      <li>Attestation de fin de formation d\u00e9livr\u00e9e<\/li>\n    <\/ul>\n\n  <\/div>\n<\/div><\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>D\u00e9ployer une application IA (ML \/ NLP) en production. Objectifs : Packager un mod\u00e8le ML et concevoir une API d&#8217;inf\u00e9rence [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-1088","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/pages\/1088","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/comments?post=1088"}],"version-history":[{"count":7,"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/pages\/1088\/revisions"}],"predecessor-version":[{"id":1095,"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/pages\/1088\/revisions\/1095"}],"wp:attachment":[{"href":"https:\/\/pythonia.fr\/index.php\/wp-json\/wp\/v2\/media?parent=1088"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}