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 teams standardize SageMaker for production… Bedrock support coming soon.
AWS Certified ML Specialist helping teams cut SageMaker spend and reduce risk with repeatable, code-based deployments.
Clear deliverables, fast 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
Common SageMaker issues we see in production—and quick wins to reduce spend and strengthen security.
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, encryption, and network settings drift across projects. A repeatable code-based baseline keeps SageMaker secure and audit-ready as you scale.
From audits to ongoing optimization, we reduce SageMaker spend and strengthen security.
A fast, high-signal review to identify quick wins for cost and security—plus the right next step for your environment.
A comprehensive SageMaker audit to reduce spend and strengthen your security posture.
Credit: 100% of the audit fee is applied toward baseline deployment if you move forward within 60 days.
Book an Audit CallDeploy a production-ready SageMaker foundation through code - secured by default and built to scale.
Best for teams running SageMaker in production and scaling usage over time.
Ongoing cost + security check-ins to keep your SageMaker environment efficient, secure, and aligned as usage grows.
Starting at $1,500/month
Monthly review • Cost trending • Security drift checks • Recommendations
Pricing scales with accounts, environments, and support level.
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.
Book a free 15-minute call. We’ll review your SageMaker setup and recommend the right next step—readiness review, audit, baseline deployment, or ongoing support.