MLOps & ML Systems
Turn models into reliable products. Standardize delivery, add observability, and govern the lifecycle so you hit SLOs without runaway cost.
The Challenge
Ad-hoc pipelines, brittle deployments, limited monitoring, and compliance friction slow releases and inflate spend.
Our Approach
Platform patterns, golden paths, and controls for train/serve/evaluate. Observability and guardrails that fit your environment.
Key Outcomes
Faster, Safer Releases
Reusable pipelines reduce cycle time and errors.
Operational Confidence
Registries, approvals, and rollbacks that keep risk in check.
Deep Observability
Quality, drift, latency, and cost visibility to act early.
Sustainable Cost
FinOps practices and right-sizing across environments.
Compliance Ready
Evidence and documentation integrated into delivery.
Core Deliverables
- • Reference platform architecture and golden paths
- • CI/CD templates with registry and approval workflows
- • Feature store & lineage approach
- • Observability KPIs and dashboards spec; incident/runbooks
- • Compliance artifacts (model cards, lineage, approvals)
Cross-links: Governance checkpoints via AI Governance & Risk; align with AI & ML Architecture.
Our Method — AtelaMind MLOps Operating Path
Map & Prioritize
bottlenecks, risks, goals
Standardize & Template
golden paths, CI/CD, registry
Instrument & Observe
metrics, alerts, dashboards
Operate & Improve
SLOs, rollback, continual hardening
Enable & Govern
ownership, training, lightweight controls
Engagement Models
Case Study
European Enterprise — Established foundations for consistent model delivery and runtime monitoring across multiple teams.
FAQs
Tooling fit?
We integrate with your stack; designs are vendor-neutral.
On-prem/hybrid?
Yes—patterns adapt to your constraints.
How are you different from big firms or tool vendors?
Independent patterns over product push; senior engineers only; no lock-in.
Handover?
Templates and runbooks transfer to your teams.