Operate a production model with evidence beyond endpoint uptime.
RaftLabs builds a focused MLOps path around one production model: versioned data and artefacts, repeatable training, deployment controls, model and data monitoring, alert ownership, rollback, and a retraining decision. The engagement begins with the failure the team cannot currently detect or reproduce, not a platform shopping list.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
01
Can the team reproduce the exact data, code, configuration, and artefact behind the current model?
02
Does application monitoring show a healthy endpoint while nobody can tell whether predictions remain useful?
Plain answer
MLOps services make production model training, release, monitoring, rollback, and retraining repeatable. RaftLabs builds versioned pipelines, model registries, quality and drift signals, deployment gates, alert ownership, and runbooks around one model. A focused monitoring layer starts at $15,000 and commonly takes 8 to 10 weeks.
A healthy model endpoint can return the wrong answer all day.
Latency, error rate, and CPU show whether serving infrastructure works. They do not show whether customer behaviour changed, an upstream feature broke, labels shifted, or a new model version improved the cases the business cares about.
MLOps becomes useful when each signal reaches a decision. The team can reproduce the current model, compare a candidate, detect a meaningful change, decide whether to investigate or retrain, release through a gate, and return to the previous version when evidence fails.
Relevant delivery record
recorded reduction in clinical decision time
20%
RaftLabs patient-monitoring AI project
patients included in the delivered monitoring workflow
150+
RaftLabs project record
clinics represented in the product rollout
80+
RaftLabs project record
These figures belong to one patient-monitoring AI project and describe its recorded outcome and rollout. They are not an MLOps benchmark, independently audited model-quality result, or promise that monitoring infrastructure will reproduce the same effect. The case study describes the product context.
MLOps fits when one model already matters in production and its operating decisions are weak or manual.
Choose machine learning development when the prediction and model are still being built, or data engineering when unreliable source pipelines are the main problem.
A fit
01
A production model and business or domain owner can define the first operating boundary.
02
Training data, code, model artefacts, serving, and feedback can be inspected.
03
The team wants to own alerts, approvals, rollback, and retraining after handover.
Not a fit
01
No viable model or decision threshold has been established yet.
02
Ground truth is unavailable and nobody can define a useful proxy or review path.
03
The buyer expects drift detection to guarantee accuracy or remove human accountability.
One-model scope
What a focused MLOps layer includes
01
Lineage and reproducibility
Version training data references, code, parameters, environments, evaluation,
and model artefacts so the team can explain what is serving and reproduce a
candidate. Record limitations when source data cannot be frozen or fully
reconstructed.
02
Release, serving, and rollback
Place candidates behind evaluation gates, package runtime dependencies,
separate deployment from promotion, and retain a tested rollback path. Use
shadow, canary, or staged release only where feedback and traffic support it.
03
Quality and drift monitoring
Track input validity, feature distributions, predictions, confidence, latency,
errors, and labels or business proxies. Choose thresholds from the model's
risk and feedback delay, not from a generic drift dashboard.
04
Retraining decisions and runbooks
Connect alerts to investigation, data repair, threshold review, retraining,
validation, approval, or rollback. Automate repeatable work while keeping
consequential promotion decisions with named owners.
MLOps or machine learning development?
Build the model vs operate the model
MLOps services
ML development
Starting point
A model already matters in production
A decision needs a new predictive model
Primary question
Can we reproduce, observe, release, and recover it?
Can the data predict the outcome well enough?
Core evidence
Lineage, gates, monitoring, rollback, runbooks
Baseline, evaluation, threshold, product result
Main owners
ML, data, platform, product, domain operations
Product, data science, engineering, domain experts
Starting price
$15K for one monitoring layer
$25K for a focused production ML system
Choose machine learning development when the model and decision boundary remain the central uncertainty. Choose MLOps when the current model's lineage, release, monitoring, or maintenance is the risk. Use data engineering when source reliability and definitions must be fixed first.
Delivery
From production uncertainty to one owned model path
Four steps connect model telemetry to release and retraining decisions.
Step 1
01
Define the model decision
Choose one production model and name the quality, drift, lineage, release, or
retraining question the first phase must answer. Define owners, risk, feedback
delay, and acceptable blind spots.
Step 2
02
Audit data, artefacts, and ownership
Trace training data, code, configuration, registry, serving, labels, business
feedback, alerts, and the people who approve model changes. Identify which
versions cannot currently be reproduced.
Step 3
03
Build and exercise the operating path
Add versioning, monitoring, gates, deployment, rollback, and retraining
components, then test them with representative data, failure, candidate, and
recovery scenarios.
Step 4
04
Calibrate and hand over
Review thresholds and response decisions, document known blind spots, and
leave pipelines, dashboards, runbooks, and ownership with the operating team.
Revisit automation after real feedback arrives.
MLOps risks that tooling cannot decide
Drift has no business interpretation
Connect statistical changes to features, segments, outcomes, and owners. A distribution shift alone does not say whether a model should change.
Labels arrive too late
Use clearly labelled proxies and delayed evaluation, state their limits, and avoid presenting proxy health as confirmed prediction quality.
Retraining promotes regressions
Validate candidates against representative and protected cases, compare them with the current model, and require approval appropriate to the consequence of error.
Sensitive data leaks into telemetry
Agree allowed fields, access, retention, deletion, and audit needs with client security and privacy owners before logging examples or predictions.
Scope and price
A focused MLOps layer starts at $15,000.
Begin with one production model and one weak operating path: lineage, release, monitoring, rollback, or retraining.
Cloud compute, storage, model monitoring, feature platforms, data tools, and other vendor charges remain separate unless the proposal includes them.
Starting investment
Starts at $15K
A focused first model commonly takes 8 to 10 weeks. A broader platform starts around $50K after model count, data, feedback, serving, controls, and ownership are reviewed.
Signals map to owners and decisions
The first scope records who responds to each model signal and when the
response is investigation, repair, retraining, approval, or rollback.
No automatic quality promise
Monitoring can expose evidence and blind spots. It cannot guarantee model
accuracy, remove domain review, or make delayed ground truth immediate.
MLOps services make model development and operation repeatable across data, code, configuration, artefacts, evaluation, deployment, monitoring, rollback, and retraining. The implementation should connect technical signals to product or domain decisions. A model registry or dashboard alone does not establish that the current production model is reproducible, useful, or safely replaceable.
Machine learning development owns the prediction problem, data, features, model, threshold, evaluation, and product integration. MLOps owns the repeatable path around models after and between experiments: lineage, training, registry, release, serving, monitoring, rollback, and retraining. A new model often needs both, but an existing useful model may only need its operating path repaired.
Cloud observability shows whether requests, services, dependencies, and infrastructure are healthy and diagnosable. Model monitoring asks whether input distributions, predictions, labels, calibration, and business outcomes remain within an accepted boundary. A model API can be fast and available while its output quality has deteriorated, so production ML usually needs both views.
It should not by default. Drift may reflect seasonality, an upstream bug, a product change, or a harmless change in a low-value feature. The response can be investigation, data repair, threshold change, retraining, rollback, or no change. Automatic promotion requires representative validation and a risk level that permits it; higher-consequence models need human approval.
A focused monitoring and operating layer for one production model starts at $15,000 and commonly takes 8 to 10 weeks. A broader platform with experiment tracking, registry, several deployment paths, retraining, or feature infrastructure starts around $50,000 after model count, data systems, labels, cloud, controls, and operating ownership are reviewed.
Work with us
Bring the model change your team cannot currently prove.
We will trace its data, artefact, release, monitoring, rollback, and ownership path, then scope the smallest operating layer that closes the gap.
Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.