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AI05 · AI Infra
AI/ML Platform & GPU Workload Infrastructure
GPU scheduling, training and inference pipelines, and compliance for regulated data. Kubernetes-native, cost-aware, production-grade ML infrastructure.
$6,500
scoped like a platform build
What you get
- GPU and ML workload orchestration on Kubernetes — scheduling, device plugins, node affinity and tolerations so GPU workloads land where they belong without manual intervention.
- Training and inference pipeline automation — reproducible pipelines that move from notebook to production without rewriting the infrastructure layer each time.
- Mixed spot and on-demand capacity for cost control — training jobs on spot instances with checkpointing and automatic resumption; inference on reserved capacity for predictable latency.
- HIPAA and regulated-data ML platforms — encryption at rest and in transit, audit logging, access controls and the compliance documentation your security team needs.
- Model deployment lead time: weeks → hours — a pipeline that deploys a validated model to production with one merge, not a two-week handoff between data science and platform engineering.
Proven on regulated workloads
I built a HIPAA-compliant ML platform on AWS EKS that took model deployment lead time from weeks to hours. The platform passed its compliance reviews and has operated with zero security incidents through handover.
How it works
A fixed-scope project, priced like a platform build. We scope the infrastructure, the pipelines and the compliance controls before I start. The deliverable is a production-ready platform your data scientists and ML engineers can use without waiting on infrastructure.
Ideal for: ML teams whose data scientists are blocked on infrastructure, or AI products moving from notebook to production with compliance requirements.