Your Dedicated AI Operations Team

Full ML operations coverage on retainer — model health, infrastructure management, incident response, and quarterly performance reviews. Your AI runs reliably.

Duration: Monthly retainer Team: 1 MLOps Lead + 1 Data Engineer (dedicated)
The Challenge

You might be experiencing...

You have multiple AI models in production but no in-house ML operations team to monitor and maintain them — your data scientists are focused on research, not operations.
AI incidents (model downtime, inference API failures, data pipeline breaks) are handled reactively by your engineering team who lack ML-specific expertise.
Nobody in your organisation owns the model retraining schedule, drift monitoring, and performance reporting — it falls through the cracks between data science and engineering.
Board-level AI governance requires regular performance reporting on AI systems — you have no process to produce this.

Managed AI operations is the difference between AI as a project and AI as a capability. Projects have end dates. Capabilities require ongoing investment — monitoring, retraining, incident response, and governance — to remain valuable as the business and market evolve.

Our Approach

Engagement Phases

Month 1

Onboarding & Baseline

Audit all production AI models and pipelines. Establish monitoring baselines. Document operational runbooks. Set up incident response process and escalation paths.

Monthly

Ongoing Operations

Daily model health checks. Weekly drift monitoring reports. Incident response (SLA: 4-hour acknowledgement, 24-hour resolution for P1). Monthly performance review with retraining recommendations.

Quarterly

Quarterly Reviews

Full model performance audit. Business metric impact assessment. Roadmap review: new use cases, model upgrades, architecture improvements. Executive summary for board reporting.

What You Get

Deliverables

Weekly model health report
Monthly performance review with retraining recommendation
Quarterly AI portfolio audit with executive summary
Incident response: 4-hour SLA acknowledgement, 24-hour resolution
On-call coverage for AI infrastructure incidents
Annual model lifecycle planning and roadmap
Expected Outcomes

Before & After

MetricBeforeAfter
AI UptimeNo SLA: inference API downtime discovered by end users99.9% uptime SLA with 4-hour incident response
Model CurrencyAd-hoc retraining: models running stale for 6-12 monthsScheduled reviews: every model assessed against retraining trigger monthly
GovernanceNo board-level AI reporting — AI health is invisible at executive levelQuarterly AI portfolio report with performance, risk, and roadmap
Technology

Tools We Use

Evidently AI + Grafana PagerDuty / Opsgenie MLflow Notion / Confluence
Common Questions

Frequently Asked Questions

What is included in the Managed AI Operations retainer?

The retainer covers: daily model health monitoring, weekly drift reports, monthly performance reviews, incident response (4-hour acknowledgement SLA for P1), retraining execution when triggered, model registry management, and quarterly executive reporting. It does not include new model development (covered by Vertical AI Model Development) or major infrastructure changes (scoped separately).

What is the minimum contract term?

Six months. ML operations value compounds over time — establishing baselines, calibrating drift thresholds, and building operational knowledge about your specific models takes 2-3 months to mature. Short-term engagements do not achieve this. Annual contracts with quarterly reviews are our standard.

How does this relate to mlai.ae's other services?

Managed AI Operations is the 'Operate' capability that follows a 'Build' engagement. After we deliver a model (Vertical AI Model Development, Domain Fine-Tuning, or MLOps Pipeline Architecture), the operations retainer keeps it running reliably. Clients who built with us benefit from operational continuity — we already know the architecture, the data sources, and the model's behaviour. We also operate models built by other vendors or by in-house teams.

Can you operate models we did not build?

Yes. We onboard third-party models by documenting the architecture, establishing monitoring baselines, and building operational runbooks. Onboarding takes 2-4 weeks for a standard ML model and 4-8 weeks for complex or undocumented systems. We require access to the model artefacts, training code, and data pipelines to operate effectively.

Build It. Run It. Own It.

Book a free 30-minute AI discovery call with our Vertical AI experts in Dubai, UAE. We scope your first model, estimate data requirements, and show you the fastest path to production.

Talk to an Expert