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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.
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The Training High Availability - Maximum Resilience for AI in Production 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.
To ensure the quality of the Training High Availability - Maximum Resilience for AI in Production training, Learni provides the following 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.
The assessment of skills acquired during the Training High Availability - Maximum Resilience for AI in Production training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Dive into advanced High Availability principles applied to AI systems, exploring active-passive and active-active topologies with Kubernetes and Helm to orchestrate Deep Learning models. Perform practical exercises on node redundancy, configure resilient StatefulSets for AI vector databases, and produce an initial HA architecture diagram validated by real-world cases from leading AI enterprises.
Build ultra-resilient High Availability clusters for Deep Learning applications, integrating etcd for distributed state management and Calico for secure networking. Practice deploying multiple replicas of AI services, test fault tolerance via chaos engineering with LitmusChaos, analyze logs to optimize recoveries, and deliver a functional cluster simulating real enterprise production workloads.
Master advanced load balancing in High Availability for intensive Prompt Engineering workflows, configuring NGINX Ingress and HAProxy with dynamic health checks. Implement least-connections and IP hash algorithms to balance AI inferences, simulate traffic spikes with Locust, debug bottlenecks, and generate a performance report proving 99.99% availability for professional skills.
Deploy High Availability monitoring stacks with Prometheus, Grafana, and Alertmanager for real-time AI pipeline surveillance. Configure predictive alerts on Deep Learning metrics, automate auto-healing via Kubernetes operators, test injected failure scenarios, correct live anomalies, and produce an exportable custom dashboard for certifying enterprise infrastructure.
Integrate complete High Availability with ArgoCD for GitOps and Istio for service mesh in critical AI environments. Deploy blue-green and canary rollouts for uninterrupted model updates, secure mTLS communications for Prompt Engineering, validate resilience via automated audits, and finalize a deliverable DRP (Disaster Recovery Plan) ready for professional production certification.
Target audience
AI Systems Architects, ML DevOps Engineers, Deep Learning Infrastructure Engineers, AI Enterprise Scalability Managers
Prerequisites
Advanced mastery of Kubernetes, Docker Swarm, hybrid cloud (AWS EKS, GCP GKE), microservices architectures, and CI/CD pipelines for AI models
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