Overview
The development, training, deployment, and orchestration of modern artificial intelligence systems require highly performant, flexible, scalable, and secure computing infrastructures. Cloud computing has emerged as a major technological lever, allowing organizations to access the computational power, managed services, high availability, and scalability required for their AI projects while controlling costs and implementation timelines.
At NeuriaLabs, we support our clients in the architecture, deployment, and optimization of cloud environments dedicated to artificial intelligence, across the three main hyperscalers Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), as well as in hybrid or sovereign contexts when regulatory, industrial, or strategic constraints necessitate.
Cloud Infrastructures for AI
We design tailored cloud environments for the entire AI chain:
• Training of heavy models on GPU or TPU
(EC2 instances P4, Azure ND, GCP A100, TPU v5e), with autoscaling, task parallelization, and monitoring of metrics.
• High-performance storage for massive data
(Amazon S3, Azure Blob Storage, Google Cloud Storage), with management of service levels (tiering), access, and lifecycle policies.
• Containerization and orchestration
via Docker, Kubernetes, EKS, AKS, GKE, with integration of microservices, RESTful APIs, model servers (TorchServe, Triton, MLServer), and secure gateways.
• Managed MLOps pipelines
utilizing Vertex AI (GCP), SageMaker (AWS), Azure ML, combined with automation services (Cloud Composer, Step Functions, Logic Apps) for managing the model lifecycle (training, testing, validation, deployment, monitoring).
• Secure and segmented networks (VPC, subnet, VPN)
for the isolation of sensitive workloads, compliance with sector security standards, or interconnection with the client's internal systems.
Hybrid Environments and Edge Computing
Not all use cases can rely on 100% cloud infrastructures. That’s why we also offer hybrid or multi-cloud architectures, combining:
• Public cloud for compute or storage intensive tasks,
• On-premise or private cloud for sensitive data or regulatory constraints,
• Edge computing for processing at the edge of the network (sensors, cameras, mobile devices), with low latency and functional autonomy.
We orchestrate these environments using abstraction solutions like Anthos, Azure Arc, or AWS Outposts, allowing for unified governance, coherent deployment of models, and centralized supervision, regardless of the execution location.
Governance, Security, and Compliance in the Cloud
Mastering cloud environments requires rigorous governance, which we structure around the following principles:
• Infrastructure and access security
(IAM, MFA, logging, network segmentation, volume encryption, WAF, DDoS protection)
• Traceability and logging
(CloudTrail, Stackdriver, Azure Monitor) with log retention, anomaly detection, alerts, and regulatory compliance
• Compliance with standards and regulatory frameworks
(ISO 27001, SOC 2, GDPR, HDS, PCI-DSS, HIPAA depending on the sector), with documentation of shared responsibilities between client and provider
• Sovereignty and resilience
the ability to choose storage and compute regions, implementation of business continuity plans, inter-zone replication, and multi-level encrypted backup
Cost Optimization and Management
An AI infrastructure in the cloud must balance power and budget efficiency. NeuriaLabs assists its clients in finely managing cloud expenditures through:
• Predictive modeling of costs according to usage scenarios
• Establishing adaptive scaling policies and right-sizing of instances
• Real-time monitoring and optimization of architectures (reducing redundancy, caching, using spot or reserved instances)
• Automating the shutdown of non-production or prolonged idle environments
NeuriaLabs Approach
Our approach to cloud computing is distinctly independent, strategic, and sovereign:
• We do not rely on any single provider, which allows us to adapt our architectures to the specific constraints of each client.
• We master the three main ecosystems (AWS, Azure, GCP) as well as sovereign environments (OVH, Scaleway, 3DS Outscale, internal clouds).
• We integrate a performance logic by usage, ensuring coherence between business needs, AI requirements, and infrastructure choices.
• We support our clients in the long term, with monitoring, support, evolution, and continuous security services.
