Startups • Scale-ups • Enterprise
Cost + Security audits for AWS SageMaker, followed by a production-ready baseline deployed through code - private-by-default, encrypted, least-privileged, and audit-ready.
AWS Certified Machine Learning • Specialist
We help engineering teams cut SageMaker spend and eliminate security risk — with a live audit dashboard, exportable PDF report, and production-ready baseline deployed through code. In a typical engagement, our most common finding is an ml.m5.4xlarge real-time endpoint running at under 15% utilization — recoverable waste of $6,000–$12,000 per year, per endpoint.
AWS Certified ML Specialist with 15+ years of enterprise platform engineering across financial services and pharmaceutical environments.
Clear deliverables, 5-day turnaround, and a baseline your team can reuse across projects.
AWS Certified Machine Learning – Specialty
AWS ML Platform Specialist · SageMaker Production Readiness · Security + Cost Guardrails
These are the findings that appear in almost every SageMaker environment we audit. Our live dashboard surfaces all four — with severity ratings, savings estimates, and a prioritized fix order — delivered within 5 business days.
Instances are sized "just in case" and never revisited. Right-sizing and choosing the right inference pattern can unlock meaningful savings.
Notebooks, endpoints, and supporting resources run longer than needed. Simple guardrails and schedules reduce waste quickly.
Fixed capacity wastes spend at idle and underperforms during spikes. Auto-scaling with sensible bounds keeps costs predictable.
IAM roles accumulate over-broad permissions with each new project. In a recent audit we found a SageMaker execution role with s3:* on every bucket in the account — a single misconfiguration that would have failed any SOC 2 review.
From a focused cost audit to full cost + security coverage and production-ready baseline deployment — pick the right starting point for your environment.
Find and eliminate SageMaker overspending fast — with a live dashboard and actionable PDF report delivered in 5 business days.
Full cost and security coverage in a single five-day engagement — with multi-account support, a complete dashboard, and a remediation roadmap your team can action immediately.
Turn your audit roadmap into a production-ready SageMaker foundation — deployed through Terraform IaC, secured by default, and built to scale.
Not sure which engagement fits your environment? A 15-minute call is the fastest way to find out.
Schedule a Discovery CallFor teams post-audit or post-deployment who want ongoing cost and security coverage as their SageMaker environment grows.
Every engagement delivers a live dashboard across three views — plus an exportable PDF report your team can act on immediately.
Endpoint utilization, training job costs, notebook waste — prioritized by savings impact.
Most SageMaker deployments can save 30–50% through optimization.
Day 1
Short kickoff to confirm scope and goals. You grant read-only AWS access (setup guide provided). We inventory SageMaker resources and the surrounding AWS services that support your ML workflow.
Days 2–3
Review utilization and spend drivers (CloudWatch + cost data) and assess security posture across IAM, encryption, and network access. We identify quick wins and the highest-impact fixes.
Day 4
Deliver a prioritized findings report with savings estimates, risk notes, and a clear remediation roadmap. Includes an executive summary plus technical details your team can action.
Day 5
30-minute readout of findings and recommendations. We align on next steps—your team implements, or we deploy the secure SageMaker baseline and provide optional monthly support.
Tell us about your SageMaker setup and we'll recommend the right next step — whether that's a cost assessment, a full audit, or a baseline deployment.