L
Lemay.ai
EdgeKube Platform
Secure AI Infrastructure Automation

Build Your Own Secure AI Cloud
on Kubernetes, Anywhere

EdgeKube by Lemay.ai delivers a fully automated, GPU-ready Kubernetes platform for government and enterprise. Deploy a complete AI/ML stack on-premises or in private cloud — with the compliance, control, and observability your mission demands.

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Designed for classified environments, critical infrastructure, and regulated industries.
GPU Cluster Status Environment: On-Prem DC
Kubernetes
v1.34
Automated via Ansible
GPU Nodes
8 Online
CUDA 13 · v570 drivers
Cluster Workload Training · Inference · ETL
Provisioned with: MLflow · Airflow · JupyterLab · PostgreSQL · MinIO · ELK · Prometheus

A Turnkey AI Infrastructure Platform for Internal AI & R&D

EdgeKube is Lemay.ai’s production-ready AI Infrastructure Automation Platform, engineered for organizations that cannot depend on public cloud. Using Ansible-based orchestration and modular Kubernetes components, EdgeKube turns bare-metal or virtual machines into secure, GPU-accelerated clusters tailored to your data science, research, and internal AI workloads.

From storage, networking, and container runtime through to MLOps tooling and observability, every component is automated, reproducible, and validated to enterprise and government-grade standards.

1
Air-gapped or Private Cloud
Deploy in classified networks, sovereign data centers, or regulated private clouds.
2
Built-in Governance
RBAC, namespace isolation, and secrets management aligned with internal security policies.
3
Infrastructure as Code
Environments are defined in Ansible and Helm for repeatable Dev, QA, and Production deployments.

Key Platform Capabilities

EdgeKube automates the full lifecycle of your AI infrastructure: from cluster provisioning and GPU enablement to MLOps tooling and observability for mission-critical workloads.

Kubernetes Automation

Automated, Hardened Kubernetes Clusters

  • Provision Kubernetes v1.34.x across Ubuntu 24.04+ nodes via Ansible.
  • Preconfigured Calico networking and containerd runtime.
  • Hardened SSH access and repeatable setup/teardown routines.
  • Lifecycle management for Dev, QA, and Production clusters.
GPU Acceleration

GPU-Ready for Training & Inference

  • Automated NVIDIA driver (v570+), CUDA 13.0, and Container Toolkit install.
  • Support for GPU Operator and Device Plugin for scheduling and monitoring.
  • Built-in test pods validate GPU readiness for AI/ML workloads.
  • Scale worker pools as new compute is added.
Modular Data & MLOps

A Complete Internal AI Platform Stack

  • PostgreSQL, MinIO, MLflow, Apache Airflow, and JupyterLab.
  • ELK + Prometheus for logs, metrics, and infrastructure health.
  • ZFS-backed persistent SMB storage optimized for mixed workloads.
  • Composable design to match your internal architecture standards.

Core Platform Components

Every module is tuned for secure, internal data products and AI systems deployed inside your governance perimeter.

Component Function
PostgreSQL on Kubernetes Reliable backend for structured data, metadata, and internal applications.
MinIO Object Storage S3-compatible repository for versioned datasets, models, and artifacts.
MLflow Tracking Server Central registry for model lifecycle management, experiments, and lineage.
Apache Airflow ETL and data-pipeline automation for research and internal production systems.
JupyterLab Secure workspaces for data scientists and researchers, with persistent volumes and RBAC.
ELK Stack + Prometheus End-to-end observability for logs, metrics, and infrastructure health across the cluster.

Built for Government & Enterprise AI Teams

EdgeKube gives internal teams a unified foundation for data processing, model development, and secure experimentation — while keeping sensitive data inside your own perimeter.

Internal ETL & Analytics Pipelines

Automate ingestion, transformation, and analysis for internal systems and mission data.

  • Airflow orchestrates multi-stage ETL and data quality workflows.
  • PostgreSQL and MinIO act as durable system-of-record stores.
  • Reproduce data flows across Dev, QA, and Production namespaces.

Model Development & Versioning

Train and evaluate models on GPU-enabled clusters fully controlled by your organization.

  • MLflow tracks datasets, model versions, and experiment results.
  • GPU operators ensure efficient scheduling across nodes.
  • Reproducible pipelines from research to deployment.

Secure On-Prem AI Compute

Run AI workloads inside compliant, air-gapped, or sovereign environments.

  • RBAC, namespace isolation, and Vault-integrated secret management.
  • Cluster policies tuned for government and critical infrastructure.
  • Centralized logging and metrics for audit and assurance.

Data Science Sandboxes & Internal Tools

Give teams safe, persistent workspaces and realistic test environments.

  • Isolated JupyterLab instances per user or team.
  • Shared namespaces for collaborative R&D with clear guardrails.
  • Recreate production data flows for QA and CI/CD validation.

Why Partner with Lemay.ai

Lemay.ai has delivered AI solutions to clients operating in regulated and security-sensitive domains. EdgeKube packages that experience into a platform you can deploy in your own environment — supported by experts who understand both AI and production operations.

  • End-to-End Delivery: From infrastructure design and cluster automation to MLOps hardening and knowledge transfer.
  • Security-First Design: Configurations aligned with common government and enterprise security practices, including segmentation and least-privilege access.
  • Training & Handover: We upskill your internal teams to operate, extend, and govern the platform independently.

Technical Highlights

  • Infrastructure as Code: All environments defined and version-controlled with Ansible and Helm.
  • Persistent Storage: ZFS-backed SMB volumes for sequential I/O and mixed data workloads.
  • Scalability: Expand worker pools and storage as new pipelines and teams come online.
  • Reproducibility: Consistent deployment across Dev, QA, and Production clusters.
End state: a fully operational, validated AI/ML infrastructure with GPU-ready Kubernetes, persistent storage, automated pipelines, secure Jupyter environments, and central observability — all running under your control.

See EdgeKube in Action — or Plan Your Deployment

Whether you’re modernizing an on-prem data center, building an internal AI platform, or enabling secure R&D environments, Lemay.ai can tailor EdgeKube to your mission and compliance requirements.

Start with the live demo, then schedule a technical session to discuss your architecture, security posture, and rollout strategy.

Open Live Demo Contact Lemay.ai