AI Infrastructure Services

SGD 0.00

Raydian Cloud’s AI Infrastructure Services are purpose-built to help our customers architect, deploy, and operate robust environments for AI and ML workloads. Whether you're modernizing legacy systems or launching greenfield AI initiatives, our end-to-end offerings ensure your infrastructure is optimized for performance, scalability, and cost-efficiency.

Raydian Cloud’s AI Infrastructure Services are purpose-built to help our customers architect, deploy, and operate robust environments for AI and ML workloads. Whether you're modernizing legacy systems or launching greenfield AI initiatives, our end-to-end offerings ensure your infrastructure is optimized for performance, scalability, and cost-efficiency.

🧠 Consultancy & Strategy

  • AI Readiness Assessment: Evaluate current infrastructure, data pipelines, and workload characteristics to determine AI suitability.

  • Architecture Advisory: Define scalable, cloud-native or hybrid architectures tailored to AI/ML use cases.

  • Platform Selection & Sizing: Recommend optimal compute, storage, and networking configurations across public cloud, on-prem, or edge environments.

  • Security & Governance Planning: Establish policies for data privacy, model integrity, and compliance across AI workflows.

🛠️ Design & Engineering

  • Infrastructure Blueprinting: Design high-performance environments for training, inference, and data processing.

  • Data Pipeline Integration: Architect seamless ingestion, transformation, and storage layers for structured and unstructured data.

  • AI Platform Enablement: Integrate popular frameworks (e.g., TensorFlow, PyTorch, MLflow) with Kubernetes, GPU clusters, and orchestration tools.

  • Resilience & Scalability Design: Build fault-tolerant, elastic systems that adapt to dynamic AI workloads.

🚀 Implementation & Deployment

  • Cloud & Hybrid Rollouts: Deploy infrastructure across AWS, Azure, GCP, or hybrid environments with automation and IaC best practices.

  • GPU & HPC Cluster Setup: Provision and configure compute-intensive environments for model training and simulation.

  • CI/CD for ML Ops: Implement pipelines for continuous integration, testing, and deployment of models and data workflows.

  • Monitoring & Observability: Integrate tools for real-time performance tracking, anomaly detection, and cost optimization.

🔧 Managed Services & Optimization

  • 24/7 Infrastructure Monitoring: Proactive health checks, alerting, and incident response to ensure uptime and reliability.

  • Performance Tuning: Ongoing optimization of compute, storage, and network resources to meet evolving AI demands.

  • Patch Management & Upgrades: Regular updates to infrastructure components, drivers, and AI frameworks.

  • Cost & Resource Governance: Implement policies and automation to control spend and maximize ROI.