Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Formation Compute Engine 2026 - Déployer IoT scalables training are: Maîtrise avancée de Google Cloud Platform, Kubernetes et Docker, expérience en protocoles IoT comme MQTT et LoRa, gestion de capteurs connectés. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Compute Engine 2026 - Déployer IoT scalables training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Compute Engine 2026 - Déployer IoT… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

Configure your training

Response within 24hNo commitment100% free

8 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Formation Compute Engine 2026 - Déployer IoT scalables training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Formation Compute Engine 2026 - Déployer IoT scalables training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Formation Compute Engine 2026 - Déployer IoT scalables training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Maîtriser Compute Engine 2026 pour infrastructures IoT professionnelles

Développer des déploiements scalables de capteurs et sensors en entreprise

Concevoir des architectures hybrides MQTT-LoRa sur Compute Engine

Optimiser les performances et la sécurité des flux IoT certifiants

Implémenter des automatisations avancées pour compétences expertes

Déployer des solutions IoT résilientes en formation professionnelle

Training program

Module 1Architecture : Fondamentaux avancés Compute Engine 2026 (GCP tools, scaling IoT)

Découvrez les évolutions de Compute Engine 2026 pour IoT, configurez des instances custom avec GPU pour traitement capteurs, simulez des charges MQTT massives via exercices pratiques, analysez les métriques scaling en temps réel, produisez un diagramme d'architecture livrable, intégrez LoRa gateways pour flux hybrides, testez la résilience sous Terraform.

Module 2Réseaux : Intégration MQTT-LoRa dans Compute Engine 2026 (brokers, protocoles sécurisés)

Plongez dans les brokers MQTT avancés sur Compute Engine 2026, déployez des clusters LoRaWAN scalables, sécurisez les tunnels avec VPC et IAM, réalisez des exercices de publishing/subscribing en live, optimisez la latence pour capteurs distants, générez des dashboards Cloud Monitoring, exportez des configurations Ansible prêtes pour production entreprise.

Module 3Sécurité : Hardening Compute Engine 2026 pour IoT (zero-trust, chiffrement sensors)

Renforcez la sécurité Compute Engine 2026 avec zero-trust pour IoT, implémentez BeyondCorp et Confidential Computing, chiffrez les données capteurs en transit via MQTT-TLS, simulez des attaques LoRa et mitigations, auditez les logs avec Security Command Center, produisez un rapport de conformité GDPR, déployez des firewalls dynamiques pour environnements experts.

Module 4Automatisation : CI/CD Compute Engine 2026 IoT (Kubernetes, GitOps sensors)

Automatisez les pipelines CI/CD pour Compute Engine 2026 en IoT, intégrez GKE avec operators MQTT-LoRa, gérez les déploiements GitOps via Flux, testez des workloads capteurs en blue-green, monitorez avec Prometheus-Grafana, générez des manifests Helm personnalisés, validez la scalabilité auto-healing en cas réels entreprise.

Module 5Optimisation : Performance Compute Engine 2026 IoT (coûts, ML sensors)

Optimisez les coûts Compute Engine 2026 pour IoT massif, appliquez spot VMs et committed use, intégrez ML inference sur capteurs via Vertex AI, benchmarkez MQTT-LoRa throughput, réduisez la latence de 40% via tuning, produisez un business case chiffré, déployez une stack finale certifiante avec monitoring prédictif.