06 / Managed Data + AI
Managed Data, MLOps & DataOps Services
Managed data and AI services — including MLOps and DataOps — keep production pipelines, dashboards and models running reliably after launch, not just after the first release. Quorlytic defines service ownership, monitors business-facing outcomes rather than only infrastructure health, and handles every change through a controlled release, review and recovery process.
By QuorlyticUpdated
Give production data and AI systems a clear operating model. Define what is monitored, who responds, how changes are released and how the service improves over time.
- MonitorFreshness, volume, SLOs
- DetectAlert on the business signal
- DiagnoseLineage to the cause
- ResolveRunbook, owner, fix
- OptimizeCost and reliability
Service ownerSLOs per data product
- Reliability
- Quality
- Cost
- Pipelines
- Models
- Security
- Performance
The business problem
Inventory pipelines, dashboards, platforms and models. Separate application operations from cloud-provider responsibility and the customer's business approvals. An agreed service catalogue prevents an unclear support promise from becoming an unlimited obligation.
What we build
A successful job does not guarantee complete data. Monitor freshness, reconciliation, dashboard availability and model behavior alongside infrastructure health. Route alerts by severity and business impact, with documented escalation and recovery actions.
The outcome
Service catalogue and responsibility matrix
When this is the right engagement
Symptoms that bring teams here.
Managed operations become the right answer when the platform works but running it has become a tax on the people who built it.
The person who built the pipeline is the only one who can fix it
Operational knowledge never left its author, so there is a single point of failure with a calendar.
Alerts are numerous enough to be ignored
Alerting is instrumented but not tuned, so the signal has been lost in its own volume.
Incidents are resolved but never analysed
The same failure will recur, because the fix addressed the symptom.
Platform cost rises and nobody can attribute it
There is no workload-level attribution, so cost cannot be managed, only absorbed.
Models in production have no owner
Drift will be discovered by a business consequence rather than by monitoring.
Engineers spend most of their week on operations
Delivery capacity is being consumed by run work that could be structured and shared.
Capability model
The operating loop.
Operations is a loop, not a queue. Select a stage to see what it covers and what usually degrades.
01 — Operate
Run the platform to an agreed standard: scheduled work completes, access stays correct, releases ship safely.
What it produces
A documented, repeatable run process with no single owner.
What usually goes wrong
- Manual steps that only one person knows
- Environments that have drifted apart
- Access changes made directly in production
Technologies in this stage
- CI/CD
- Infrastructure as code
- Access reviews
- Runbooks
02 — Observe
Watch the things that indicate the platform is serving the business: freshness, quality, model behaviour and cost.
What it produces
Signals that lead an incident rather than confirm one.
What usually goes wrong
- Monitoring job success rather than data arrival
- No baseline, so anomalies cannot be recognised
- Cost visible only on the monthly invoice
- Model accuracy unmeasured after launch
Technologies in this stage
- Freshness monitors
- Quality tests
- Drift detection
- Cost attribution
03 — Respond
Move from alert to resolution with a named responder, a runbook and a communication path to affected consumers.
What it produces
Predictable incident handling with consumers informed.
What usually goes wrong
- No on-call rotation for data
- Consumers finding out from their own dashboard
- Recovery improvised because backfill was never rehearsed
Technologies in this stage
- On-call rotation
- Runbooks
- Backfill procedures
- Status communication
04 — Improve
Use what operations learns to reduce the next month's incident and cost load rather than absorbing it.
What it produces
A recorded reduction in recurring failure and avoidable cost.
What usually goes wrong
- Reviews that assign blame instead of changing a system
- Cost work done once and then lost
- Toil accepted as normal
Technologies in this stage
- Incident review
- Cost optimisation
- Test coverage
- Automation
Illustrative reference architecture
Reference architecture
What is actually being operated, and what is watched across it. Data and model workloads are drawn together because in practice the same team is woken for both.
Pipelines
Scheduled and streaming work that must complete and be replayable.
- Ingestion
- Transformation
- Backfills
Platform
Compute, storage, access and release path.
- Warehouse
- Lakehouse
- Access
- CI/CD
Models
Deployed models and AI services with live behaviour.
- Accuracy
- Drift
- Latency
- Cost
Observation
One place where all three are watched against a baseline.
- Freshness
- Quality
- Drift
- Cost attribution
Response
Named responder, runbook, consumer communication.
- On-call
- Runbooks
- Recovery
- Status
Improvement
The loop back: fewer incidents, less avoidable cost.
- Incident review
- Automation
- Cost work
Across every layer
- Ownership
- Service expectations
- Change control
- Cost attribution
- Documentation
The approach
- 01
Make the service boundary explicit
Inventory pipelines, dashboards, platforms and models. Separate application operations from cloud-provider responsibility and the customer's business approvals. An agreed service catalogue prevents an unclear support promise from becoming an unlimited obligation.
- 02
Monitor the business-facing result
A successful job does not guarantee complete data. Monitor freshness, reconciliation, dashboard availability and model behavior alongside infrastructure health. Route alerts by severity and business impact, with documented escalation and recovery actions.
- Pipeline and data-platform operations
- Managed BI refreshes, models and dashboard changes
- Data quality, cloud-cost review and model monitoring
- 03
Improve through controlled change
Version changes, test them before release and maintain a rollback path. Review recurring incidents and cloud-cost drivers. Separate planned improvements from break-fix work so scope, priority and capacity are visible.
- 04
Agree coverage before commitment
Service hours, response targets, recovery objectives, on-call arrangements and third-party dependencies belong in the contract. Pricing depends on the systems and support model. Round-the-clock coverage is not implied by this service description.
- 05
Data quality and observability come first
Before adding capability, establish what failure looks like: freshness, volume, schema, duplicates and reconciliation against a source of truth. Observability means those checks are tested, their results are visible to the people who rely on the data, and every alert has a documented owner and next action rather than a shared inbox.
- Quality checks defined per data product, not per table
- Quality contracts agreed with the consuming team, with thresholds and owners
- Freshness and reconciliation surfaced where consumers see them
- Alert severity mapped to a business impact and a responder
- 06
Governance needs an owner, not only a catalogue
A catalogue that nobody maintains becomes an expensive inventory. Governance works when each critical data domain has a steward, definitions are agreed where they are used, lineage is generated rather than hand-drawn, and retention and access decisions have a recorded rationale. Tooling supports that; it does not substitute for it.
- A named steward per critical data domain
- Definitions reviewed with the teams that consume them
- Change control for definitions, policy and access, with a recorded approver
- Retention and access decisions recorded with their reason
- 07
MLOps and DataOps share one release discipline
Pipelines, dashboards and models all change production behaviour, so they should move through the same controlled path: versioned change, review, tested promotion, observable release and a tested way back. DataOps covers pipeline and data-product operations; MLOps adds model, feature and evaluation versioning. Incident response then has one process instead of three.
- One promotion path for pipelines, dashboards and models
- Rollback rehearsed, not assumed
- Incidents reviewed for a recurring cause, not only closed
Delivery model
How the work is sequenced.
Transition sequenced so responsibility moves only once operations can actually be performed.
| Phase | Activities | Decisions made here | What you keep |
|---|---|---|---|
| AssessEstablish what is being run and how well. |
|
| Operations inventory and baseline |
| ArchitectDesign the operating model before taking it on. |
|
| Operating model, runbooks and monitoring design |
| BuildInstrument before taking responsibility. |
|
| Instrumented monitoring and tested recovery procedures |
| OperateRun it, and reduce the load month over month. |
|
| Operating cadence, incident reviews and cost reporting |
Technology fit
What each technology is here to do.
Operational tooling by role. The hard part is thresholds and ownership, not instrumentation.
Detection
- Freshness monitors
- Quality tests
- Drift detection
- Log and metric collection
Detecting a problem before a consumer does is the entire value of the function.
Incident response
- Alert routing
- On-call rotation
- Runbooks
- Status communication
An alert without a named responder and a procedure is a notification, not a control.
Release
- CI/CD
- Infrastructure as code
- Environment parity
- Change review
Most data incidents originate in a change, so the release path is an operational control.
Cost
- Workload attribution
- Warehouse sizing
- Storage lifecycle
- Query analysis
Cost that cannot be attributed cannot be managed, only absorbed.
Model operations
- Accuracy tracking
- Retraining
- Model registry
- Evaluation re-runs
Deployed models degrade. Without monitoring that degradation reaches the business first.
Platform detail
Technology marks belong to their respective owners. No partnership, certification or endorsement is implied.
Engineering concerns
What has to be answered before it is trusted.
What a managed operations arrangement has to define before it can be relied on.
- Ownership boundary
- Written agreement on what is operated, what is escalated and what remains with your team.
- Service expectations
- Freshness and response expectations stated per data product, because not everything deserves the same urgency.
- Incident process
- A named responder, a runbook and a consumer communication path for every severity level.
- Change control
- Releases reviewed and reversible, since change is the most common cause of a data incident.
- Cost governance
- Workload-level attribution reported on a cadence, so cost is discussed before it is escalated.
- Knowledge transfer
- Documentation and rehearsal, so your team can take operations back whenever it chooses to.
Where it applies
Related work, and why it is related.
Where managed operations is the right arrangement, and what it connects to.
- Platform cost optimizationThe bounded engagement when cost, rather than reliability, is the reason for the conversation.
- LogisticsOperational decisions run on a clock, so freshness failures have same-day consequences.
- Financial servicesChange control and evidence of correctness carry regulatory weight as well as operational weight.
- AI and machine learningDeployed AI needs evaluation re-runs, cost monitoring and incident response as a standing function.
- Data engineeringOperations is easier when the pipelines were built to be replayed, monitored and owned.
Agree who can act before an incident occurs.
A monitoring signal needs an accountable decision maker and an authorized action. This hypothetical responsibility matrix illustrates a proposed operating boundary for a data pipeline. Quorlytic is a proposed operator only where a signed scope assigns that role; the matrix is not a statement of available staffing or contracted support.
Scope and assumptions: Agree staffing, access, support hours, escalation contacts and exclusions in the engagement scope. This example establishes no response time, service level or coverage commitment.
| Dimension | Customer owner / approver | Proposed operator | Provider boundary / exclusion |
|---|---|---|---|
| Data-quality incident | Data owner defines the invariant, business impact and whether a consumer must pause. | If assigned, collect detection evidence, identify affected runs and escalate to the named decision maker. | A successful platform job does not certify business data quality. Correctness remains a customer acceptance decision. |
| Change and release | Named change approver authorizes the change window, validation plan and recovery condition. | If authorized, prepare a reviewed change and record the execution and validation evidence. | Provider service operation is distinct from application logic. No production write or schema change is authorized by an alert alone. |
| Platform escalation | Account owner retains the vendor relationship, support entitlement and business escalation authority. | If in scope, assemble timestamps, run identifiers and sanitized diagnostics for the approved escalation route. | Vendor incident resolution follows the customer's provider agreement; the operator cannot promise a provider resolution time. |
| Cost exception | Budget owner approves consumption constraints and acceptable workload trade-offs. | If assigned, document the anomaly and recommend a separately approved investigation or experiment. | No automatic shutdown, capacity change or savings commitment without an explicit policy and authorization. |
| Access and exit | Security owner approves least-privilege access, revocation and evidence-retention requirements. | If contracted, maintain the agreed runbook and handover record; identify outstanding incidents and access to revoke. | Account ownership, retained data and deletion decisions stay with the customer. Offboarding does not imply deleting business records. |
At narrow widths, scroll the worksheet to read every column. With the table focused, use the arrow keys to scroll.
Worked incident: a hypothetical orders pipeline reports a successful run, but a reconciliation check detects duplicate business keys. The proposed operator records the run, affected dataset, check definition and known downstream consumers. The data owner decides whether to pause consumption; a change approver authorizes any deduplication or replay. Closure requires the owner to accept corrected results and the retained evidence, not merely a green job status.
- Name a primary escalation contact and an agreed fallback before granting operating access. Define what happens when approval is unavailable; do not infer permission to change business data.
- Databricks operational guidance emphasizes release processes, monitoring, alerting and logging. Those practices inform this example but do not establish Quorlytic's coverage, staffing or service terms.
When a cost exception needs a bounded diagnosis, use the cost-assessment evidence worksheet. Ongoing operating responsibility still needs a separate agreed scope.
Official references for this example:
Questions
Questions worth asking.
Can you take over an existing platform?
Begin with an operational assessment covering documentation, access, unresolved incidents, tests and dependencies. A transition period may be needed before support commitments can be agreed.
Is 24/7 support included?
No coverage schedule is assumed. Service hours and response targets must be explicitly agreed and staffed. Cloud bills, software licenses and major development work are separate unless contracted.
What should a managed service cover first?
The systems whose failure someone would notice today: production pipelines with a business consumer, dashboards used in a recurring decision, and models whose output is acted upon. Start with monitoring, ownership and a tested recovery path for those, and extend coverage deliberately rather than sweeping in every asset at once.
Next step
Discuss platform operations.
Bring your current alert volume and who is on call for data today.