MLOps & AI Infrastructure Consulting
Your models deserve
production-grade infrastructure.
I help teams build robust MLOps platforms and on-prem AI infrastructure — so your models actually ship, scale, and stay reliable.
The Problem
Your ML models work in notebooks.
Production is a different story.
Stalled Deployments
Models sit in notebooks for months. The gap between "it works locally" and "it runs in production" keeps growing.
Unstable Pipelines
Training jobs fail silently. Data pipelines break over weekends. Retraining is manual and error-prone.
No Observability
Models decay without anyone noticing. There's no drift detection, no performance tracking, no alerts — until something breaks in production.
Compliance Gaps
Model lineage is undocumented. Data versioning is ad-hoc. Audits are a scramble every time.
GPU Waste
Expensive GPU hardware sits idle or is poorly scheduled. Training jobs queue inefficiently while costs climb.
Hiring Bottleneck
The skillset spanning ML engineering, DevOps, and platform engineering is rare. Open roles stay unfilled for months.
Packages
Engagement models that fit your stage.
Each engagement is modular. Start with an assessment, move into a platform build, and hand off to your team with confidence.
Infrastructure Assessment
A focused evaluation of your current ML infrastructure, pipelines, and deployment workflows. You get a clear roadmap with prioritized recommendations.
- Architecture review & gap analysis
- GPU utilization & scheduling audit
- Pipeline reliability assessment
- Prioritized improvement roadmap
- Executive summary & technical report
Platform Build
Hands-on engineering to build or overhaul your MLOps platform. Kubernetes-native, GPU-optimized, fully observable.
- Kubernetes cluster with GPU-aware resource management
- Fair-queued distributed training & high-performance inference
- CI/CD for model deployment
- Monitoring & alerting (Prometheus, Grafana)
- Data versioning & model registry (DVC, MLflow)
- Infrastructure as Code (Terraform, Ansible / Pyinfra)
Team Enablement
Transition the platform to your team. Hands-on training, documentation, and pairing to make your engineers self-sufficient — including AI-assisted development workflows.
- Runbooks & operational documentation
- Hands-on training sessions for your team
- AI-coding workflow setup & enablement
- Pair programming & knowledge transfer
- 30-day post-handoff support
Technology
Built on the stack that scales.
The biggest ROI comes from getting the lower-level infrastructure right. Real platform engineering, all the way down.
About
Infrastructure engineer.
Hands-on delivery.
I'm Vladislav Supalov. I've spent over a decade building infrastructure and DevOps systems. Now I focus on the intersection of platform engineering and machine learning — helping teams that have built models but struggle to run them reliably at scale.
I work as a small, focused engagement — typically 1–3 months — embedding with your team to build real, working infrastructure. The goal is always to leave your team more capable than I found them.
Let's get your models to production.
Whether you need an assessment of your current setup, a full platform build, or help getting your team up to speed — let's talk.
Or find me at vsupalov.com