Managed Services · Cloud & Data

Managed operations for
cloud, data & AI.

Q: Do we have to engage all three tracks?

No. CloudOps, DataOps and MLOps are independently engageable — start with the one that carries the most operational risk today and extend as your estate matures. Coverage windows and commercial model are set per track.

Q: How does this differ from SAP AMS?

SAP AMS is application-management around the SAP estate — helpdesk, functional support, technical Basis/HANA, and (optionally) L4 Managed Operations. Cloud & Data AMS is platform-and-model operations around AWS, the lakehouse and production ML. Same 24/7 delivery model, different domains.

Q: What does MLOps actually cover?

Everything after a model is in production: registry and versioning, automated retraining, drift monitoring, evaluation against the anchor KPI, roll-back procedures, GenAI guardrails, and Amazon SageMaker / Bedrock endpoint operations. Model build stays in the Data & AI practice; this is the runtime team.

Q: Is coverage AWS-only or does it include other clouds?

CloudOps is AWS-first — that's where the deep partnership sits. Azure or GCP workloads can be covered under a scoped agreement where a customer standard requires it, but the runbooks and accelerators are AWS-native.

Q: How long is onboarding?

6–8 weeks per engaged track — discovery, shadow, parallel run, cutover. Pager coverage handed over from day one of parallel run so there's no gap in operational cover.

24/7 operations across your AWS estate, data platforms and production ML — one accountable partner, three specialised delivery tracks.

AWS Advanced Tier PartnerCloudOps · DataOps · MLOps24/7 · MEA · Caspian

Why this practice

The cloud didn't come with an ops team attached.

Landing on AWS, standing up a lakehouse, and shipping a first ML model are all worth celebrating — and none of them include the 24/7 team that keeps them running through weekends, cost spikes, drift events and audit windows.

Phoenix runs that operating layer across three specialised tracks: CloudOps for the AWS estate, DataOps for the lakehouse and pipelines, and MLOps for production models. Engage one, engage all three, or start with one and extend as your estate grows.

24/7

On-call coverage across CloudOps, DataOps and MLOps

Specialised tracks — engage independently or together

AWS

Advanced Tier Partner — Well-Architected reviews included

Accountable delivery lead across every track you engage

Three tracks

One accountable partner — three specialised delivery tracks.

Tracks are modular. Engage the ones you need today; extend as your estate matures. Each track has its own pager, its own runbooks, and its own SLA matrix — all under a single service lead.

CloudOps

AWS estate operations

24/7 platform ops, cost, resilience and security across your AWS accounts.

Landing zones aren't a project — they're a live surface. CloudOps operates the AWS estate day-to-day: account guardrails, incident response, patching, cost anomalies, DR drills, and the Well-Architected posture that keeps auditors happy.

Account & landing-zone operations, guardrails and least-privilege access
Incident response with clear P1/P2/P3 SLAs and named on-call
FinOps — anomaly alerts, right-sizing recommendations, savings-plans management
Backup, DR and business-continuity — including regular drills
Security operations — GuardDuty, Security Hub, Config; patch management
Well-Architected reviews on a defined cadence

DataOps

Lakehouse & pipeline ops

The data platform stays reliable, governed and fast — every day, not just at go-live.

DataOps runs the platform that powers analytics and AI: pipelines, catalog, quality, cost, and access. Phoenix engineers hold the pager for pipeline failures, monitor freshness and quality, and evolve the platform as new sources land.

Pipeline reliability — freshness, quality, retry and backfill playbooks
Data catalog & lineage upkeep — AWS Glue, Lake Formation, business glossary
Data-quality checks with per-table thresholds and owner routing
Governance — PII masking, row/column ACLs, audit readiness
Cost & performance — Redshift/EMR/Athena tuning, storage tiering
New-source onboarding under a repeatable pattern

MLOps

Production ML operations

Models in production behave — with retraining, drift monitoring and evaluation you can trust.

Getting a model into production is the halfway line. MLOps keeps it useful: automated retraining, drift monitoring, evaluation against real KPIs, guardrails for GenAI, and roll-back procedures when a release regresses.

Model registry, versioning, and release/rollback under change control
Automated retraining pipelines — schedule- and drift-triggered
Data-drift and concept-drift monitoring with alerting
Evaluation harness against the anchor KPI, not just accuracy metrics
GenAI guardrails — hallucination checks, prompt safety, audit trail
Amazon SageMaker & Bedrock operations — endpoint health, cost, quotas

Service levels

Priority-based response & resolution commitments.

Grouped by criticality, not by track. Critical incidents first, standard next, supporting layers last. Coverage windows and RTOs are tuned per contract against your platform posture.

P1 · Critical incidents24/7

Production tier-1 down, revenue or safety impact — response in minutes, resolution measured against a signed RTO.

CloudOps (P1)

< 15 min

Response

< 4 hrs

Resolution

Tier-1 AWS workload outage, region-level impairment, security incident.

DataOps / MLOps (P1)

< 30 min

Response

< 8 hrs

Resolution

Broken pipeline blocking a business-critical dashboard, model down for a live decision surface.

Standard incidents

P2 · HighBusiness hours

CloudOps< 2 hrs response · < 24 hrs resolution

Degraded workload, non-blocking cost spike, patchable vulnerability.

DataOps / MLOps< 4 hrs response · < 48 hrs resolution

Delayed pipeline (SLO breach), drift alert, retraining failure.

P3 · MediumBusiness hours

All tracks< 8 hrs response · < 5 business days resolution

Enhancement request, tuning task, new-source onboarding, backlog work.

Supporting layers

Change requestsBusiness hours

< 2 business days response · Per change window resolution

Standard cloud, data or ML change under CAB — sized, approved and scheduled against your release calendar.

Evolution backlogAligned to client hours

Sprint-cadence response · Per sprint resolution

Landing new sources, model iterations, cost optimisations and platform hardening — run as a continuous backlog.

RTOs, change-window cadences and business-hours definitions are fixed per contract against your operating model and time zones across MEA and the Caspian.

Commercial shapes

How Phoenix structures the engagement.

Same operating team behind three commercial shapes. Actual pricing is confirmed in the scoping workshop against your specific estate.

Predictable scope

Tiered flat monthly

Fixed monthly fee per track (CloudOps / DataOps / MLOps), scaled by estate size — accounts, pipelines and models under management.

Best for: Steady-state estates that value budget predictability.

Dedicated capacity

Named-FTE model

Named Phoenix engineers — onshore, offshore or hybrid — embedded per track against agreed roles and rotations.

Best for: Complex or fast-moving estates that need continuous dedicated coverage.

Consumption-linked

Ops + build blocks

Base run-fee for pager and SLA coverage, plus pre-purchased engineering-hour blocks for backlog work and enhancement.

Best for: Estates in build-out mode where evolution work outpaces steady-state.

Onboarding

Six to eight weeks — pager cover from day one.

Structured transition per engaged track. Day-one pager coverage is maintained throughout via the incumbent-Phoenix overlap.

Weeks 1–2

Discovery & inventory

AWS accounts, pipelines, models and SLAs mapped. Runbooks and monitoring gaps identified. Scope and coverage windows fixed per track.

Weeks 3–4

Shadow ops

Phoenix engineers shadow the incumbent team on live incidents and changes; alerting and paging integrated to the Phoenix tooling.

Weeks 5–6

Parallel run

Phoenix takes primary on incidents under incumbent oversight. SLA dashboards live; escalation paths tested end-to-end.

Week 7+

Full cutover

Phoenix owns the pager per track. Monthly service reviews, quarterly Well-Architected and platform-health checks embedded.

Governance

How the service stays honest.

Monthly service review

SLA performance, incident trends, root-cause analysis, cost trajectory and Well-Architected posture reviewed jointly with stakeholders.

Quarterly platform health

AWS Well-Architected review, data-platform audit (freshness, quality, cost), and MLOps model-health review (drift, retraining cadence).

Continuous-improvement backlog

Recurring incidents converted into permanent fixes or platform improvements via a managed change-request pipeline.

Runbook & documentation ownership

A living repository of runbooks, architecture decisions, and playbooks — owned jointly with the client, versioned in the client's own repo.

Why Phoenix

The team behind the pager.

AWS Advanced Tier Partner

AWS-native operations built on AWS best practice — MAP-eligible for migrations, Well-Architected reviews delivered in-team.

SAP heritage matters here too

The DataOps track integrates SAP as a first-class source. When your data comes out of SAP, Phoenix reads it correctly on day one.

MLOps by builders

Same team that ships Smart OCR and EstateEdge into production — MLOps is a day-job, not a book chapter.

Regional footprint

Delivered from Cairo (HQ) with cover from Riyadh, Dubai, Doha, Muscat and Baku — MEA + Caspian time-zones without hand-offs.

Next steps

Let's scope your Cloud & Data AMS engagement.

Scoping workshop

Half-day session to map your AWS estate, data platform and ML production surface, and define in-scope tracks and coverage.

Tailored proposal

Formal proposal with track selection, SLA matrix, commercial-model recommendation and transition timeline specific to your environment.

Onboard & go-live

6–8 week structured transition per engaged track, with pager coverage handed over from day one of parallel run.

Book the scoping workshop

Customer stories

Customers on this journey.

Real Phoenix engagements — measured outcomes at MEA scale.

Pharmaceutical distribution·Egypt

Pharmaoverseas Group

Egypt's largest pharma distributor moves SAP to the cloud.

2,500 Users digitalized50% Uptime improvement

Manufacturing (Plumbing)·Egypt · Germany

EGIC Group

Plumbing market-leader upgrades SAP to RISE on AWS in a month.

80% Uptime improvement100% Hardware cost reduction

Real Estate·Egypt · MENA

Arab Developers Holding

Real-estate group refactors SAP to HANA — with GenAI on top.

90% Backup time reduction11×9s Data durability (S3)

See all case studies →

FAQ

Cloud & Data AMS · frequently asked

Do we have to engage all three tracks?