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Subject: AWS Media & Entertainment : Quoi de Neuf
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From: "Marchand, Guillaume" <gmarchan@amazon.fr>
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Date: Fri, 28 Jun 2024 17:18:41 +0200
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                       <div><span>Newsletter Media & Entertainment du lundi 12 janvier 2026 au vendredi 23 janvier 2026</span>
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                       <div><span>Innovations AWS : streaming automatisé, migration Iceberg, Kubernetes simplifié et Generative AI avancée</span>
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                    <div><span>Cette newsletter présente les dernières avancées AWS en automatisation de streaming en direct, migration de data lakes vers Apache Iceberg, orchestration de clusters Kubernetes avec GitOps, observabilité serverless avec OpenTelemetry, architectures multi-agents pour la Generative AI, et applications d'IA dans l'analyse sportive professionnelle.</span>
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          <!-- BEGIN MODULE: Content 17 -->
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                       <div><span>Media, Sport and Video</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/media/schedule-aws-elemental-medialive-channel-operations-with-aws-eventbridge/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Planifier les opérations de canaux AWS Elemental MediaLive avec AWS EventBridge</span>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/media/schedule-aws-elemental-medialive-channel-operations-with-aws-eventbridge/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Ce blog post explique comment automatiser la planification et la gestion des canaux AWS Elemental MediaLive en utilisant une architecture serverless avec AWS Lambda, Amazon EventBridge et AWS Step Functions, permettant de réduire les coûts opérationnels en démarrant et arrêtant les canaux de streaming uniquement lorsque nécessaire, tout en incluant une vérification de l'état du stream.</span>
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                       <div><span>Analytics</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/big-data/enterprise-scale-in-place-migration-to-apache-iceberg-implementation-guide/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Migration sur place à l'échelle de l'entreprise vers Apache Iceberg : Guide d'implémentation</span>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/big-data/enterprise-scale-in-place-migration-to-apache-iceberg-implementation-guide/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Ce guide détaillé explique comment migrer à grande échelle des data lakes basés sur Apache Parquet vers Apache Iceberg en utilisant Amazon DynamoDB comme mécanisme d'orchestration centralisé, avec deux approches complémentaires (migrate/register_table et add_files) permettant des migrations sur place configurables, répétables et tolérantes aux pannes sans déplacement ni duplication de données.</span>
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                       <div><span>Cloud Operation</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/podcasts/aws-podcast/#751" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>#751 : Prédictions technologiques de Werner Vogels pour 2026 et au-delà...</span>
                      </a>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/podcasts/aws-podcast/#751" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Werner Vogels partage ses prédictions technologiques annuelles pour 2026 et au-delà, couvrant des sujets allant du sport et de la simulation à l'innovation énergétique intelligente, de l'informatique quantique au développement logiciel assisté par IA, ainsi que le rôle des entreprises dans la formation de la prochaine génération d'ingénieurs.</span>
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                     <a name="CloudOps" id="CloudOps"></a>
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                       <div><span>Cloud Operation</span>
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                      <a class="pc-font-alt" href="https://www.aboutamazon.com/news/policy-news-views/aws-customer-choice-multicloud-ai-tools" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>AWS s'engage en faveur du choix et de la flexibilité pour les clients, accélérés par l'IA</span>
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                   <a class="pc-font-alt" href="https://www.aboutamazon.com/news/policy-news-views/aws-customer-choice-multicloud-ai-tools" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Le paysage du Cloud Computing s'est considérablement diversifié avec l'émergence de fournisseurs européens, chinois et de nouveaux acteurs spécialisés dans l'IA, offrant aux clients une flexibilité de choix sans précédent alors que plus de 85% des services IT restent encore sur site.</span>
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                     <a name="K8s" id="K8s"></a>
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                       <div><span>Container and Kubernetes</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/containers/simplify-kubernetes-cluster-management-using-ack-kro-and-amazon-eks/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Simplifiez la gestion des clusters Kubernetes avec ACK, kro et Amazon EKS</span>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/containers/simplify-kubernetes-cluster-management-using-ack-kro-and-amazon-eks/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Ce blog post démontre comment simplifier la gestion de clusters Kubernetes en utilisant ACK, kro et Amazon EKS pour créer une solution de gestion de clusters basée sur GitOps qui standardise le provisionnement et le bootstrapping via une API Kubernetes unique, éliminant ainsi le besoin de pipelines Infrastructure as Code disjoints et permettant la gestion multi-comptes avec Argo CD.</span>
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                     <a name="DevOps" id="DevOps"></a>
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                       <div><span>DevOps</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/fr/blogs/france/exportation-de-metriques-opentelemetry-depuis-aws-lambda-vers-opensearch/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Exportation de métriques OpenTelemetry depuis AWS Lambda vers OpenSearch</span>
                      </a>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/fr/blogs/france/exportation-de-metriques-opentelemetry-depuis-aws-lambda-vers-opensearch/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Apprenez à instrumenter vos fonctions AWS Lambda avec des métriques personnalisées OpenTelemetry et à les exporter vers Amazon OpenSearch Serverless en utilisant AWS Distro for OpenTelemetry Lambda layers et Amazon OpenSearch Ingestion, permettant une observabilité flexible et indépendante des fournisseurs sans infrastructure de collecteur dédiée.</span>
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                     <a name="GenAI" id="GenAI"></a>
                      <div class="pc-font-alt" style="line-height: 121%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: 500; font-variant-ligatures: normal; color: #40be65; text-align: left; text-align-last: left;">
                       <div><span>Generative AI</span>
                       </div>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/introducing-multimodal-retrieval-for-amazon-bedrock-knowledge-bases/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Présentation de la récupération multimodale pour Amazon Bedrock Knowledge Bases</span>
                      </a>
                       </div>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/introducing-multimodal-retrieval-for-amazon-bedrock-knowledge-bases/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Amazon Bedrock Knowledge Bases introduit la recherche multimodale en disponibilité générale, permettant aux applications RAG de rechercher et récupérer des informations à travers du texte, des images, de l'audio et de la vidéo grâce aux embeddings multimodaux Amazon Nova ou à la conversion en texte via Bedrock Data Automation, le tout dans un service entièrement géré qui simplifie la construction d'applications d'intelligence artificielle exploitant des contenus multimédias d'entreprise.</span>
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                     <a name="GenAI" id="GenAI"></a>
                      <div class="pc-font-alt" style="line-height: 121%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: 500; font-variant-ligatures: normal; color: #40be65; text-align: left; text-align-last: left;">
                       <div><span>Generative AI</span>
                       </div>
                      </div>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/how-thomson-reuters-built-an-agentic-platform-engineering-hub-with-amazon-bedrock-agentcore/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Comment Thomson Reuters a construit un Hub d'Ingénierie de Plateforme Agentique avec Amazon Bedrock AgentCore</span>
                      </a>
                       </div>
                      </div>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/how-thomson-reuters-built-an-agentic-platform-engineering-hub-with-amazon-bedrock-agentcore/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Thomson Reuters a multiplié par 15 sa productivité opérationnelle en transformant ses processus manuels d'ingénierie de plateforme en un système agentique automatisé utilisant Amazon Bedrock AgentCore, atteignant un taux d'automatisation de 70% dès le lancement initial tout en renforçant la sécurité et la conformité.</span>
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                     <a name="GenAI" id="GenAI"></a>
                      <div class="pc-font-alt" style="line-height: 121%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: 500; font-variant-ligatures: normal; color: #40be65; text-align: left; text-align-last: left;">
                       <div><span>Generative AI</span>
                       </div>
                      </div>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/using-strands-agents-to-create-a-multi-agent-solution-with-metas-llama-4-and-amazon-bedrock/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Utiliser Strands Agents pour créer une solution multi-agents avec Llama 4 de Meta et Amazon Bedrock</span>
                      </a>
                       </div>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/using-strands-agents-to-create-a-multi-agent-solution-with-metas-llama-4-and-amazon-bedrock/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Ce blog post explique comment construire un workflow multi-agent de traitement vidéo en utilisant Strands Agents SDK, les modèles Llama 4 de Meta avec leurs fenêtres de contexte étendues, et Amazon Bedrock, en démontrant une architecture modulaire où des agents spécialisés collaborent pour extraire des frames, analyser visuellement le contenu, effectuer un raisonnement temporel et générer des résumés automatiques de vidéos.</span>
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                     <a name="ML" id="ML"></a>
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                       <div><span>Machine Learning</span>
                       </div>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/build-agents-to-learn-from-experiences-using-amazon-bedrock-agentcore-episodic-memory/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Créer des agents pour apprendre des expériences en utilisant la mémoire épisodique d'Amazon Bedrock AgentCore</span>
                      </a>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/machine-learning/build-agents-to-learn-from-experiences-using-amazon-bedrock-agentcore-episodic-memory/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>Amazon Bedrock AgentCore episodic memory permet aux agents IA d'apprendre de leurs expériences passées en capturant et structurant les interactions complètes (objectifs, raisonnements, actions et résultats) pour améliorer leurs performances au fil du temps, avec des résultats montrant jusqu'à 11,4% d'amélioration sur des tâches complexes grâce à un système de mémoire épisodique et de réflexion cross-épisodique.</span>
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                       <div><span>Machine Learning</span>
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                      <a class="pc-font-alt" href="https://www.youtube.com/watch?v=936vGB6MiEo" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>L'IA en action : Comment AWS AI alimente la NFL sur et en dehors du terrain | Amazon Web Services</span>
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                    <span>L'épisode « AI in the Wild » explore comment AWS AI transforme la NFL grâce au machine learning pour améliorer la sécurité des joueurs, optimiser les stratégies d'équipe et enrichir l'expérience des fans, notamment via le NFL Digital Athlete qui analyse les mouvements et les risques de blessures.</span>
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                       <div><span>Machine Learning</span>
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                      <a class="pc-font-alt" href="https://aws.amazon.com/blogs/media/how-the-nba-and-aws-built-an-ai-system-to-measure-what-actually-wins-basketball-games/" style="text-decoration: none; line-height: 142%; letter-spacing: -0.4px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 18px; font-weight: bold; font-variant-ligatures: normal; color: #151515; text-align: left; text-align-last: left;">
                       <span>Comment la NBA et AWS ont construit un système d'IA pour mesurer ce qui permet réellement de gagner des matchs de basketball</span>
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                   <a class="pc-font-alt" href="https://aws.amazon.com/blogs/media/how-the-nba-and-aws-built-an-ai-system-to-measure-what-actually-wins-basketball-games/" style="text-decoration: none; line-height: 156%; letter-spacing: -0.2px; font-family: 'Fira Sans', Arial, Helvetica, sans-serif; font-size: 12px; font-weight: normal; font-variant-ligatures: normal; color: #9b9b9b; text-align: left; text-align-last: left;">
                    <span>The NBA and AWS developed Leverage Score, an AI-powered system that uses counterfactual modeling and real-time stream processing on Amazon EKS with Apache Flink to measure which players and possessions truly drive basketball game outcomes by analyzing alternative scenarios and allocating credit across all ten players on court within seconds.</span>
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