Data ScienceThe lifecycle a model has to survive

Feature data: Versioned inputs
Train: Tracked runs in a registry
Eval gate: Promote or reject - nothing ships unmeasured
Serve: API or batch
Monitor: Drift and quality, feeding automated retrain

The brief we usually get

Two problems we are called in to fix.

Concrete starting points, each with the team we field, the stack, and what lands in the first 30 days.

Forecasting in production

Typical problemA promising forecasting notebook that nobody can run on a schedule or trust over time.

What we shipA retraining pipeline with drift detection, an eval gate, and monitoring you can read.

Example stackPythonMLflowDatabricksAzure

First 30 daysOne model in production with automated retraining and monitoring wired end-to-end.

MLOps foundations

Typical problemEvery model is a one-off; no experiment tracking, no reproducibility, no path to prod.

What we shipA model registry, CI for models, and a promotion flow from staging to production.

Example stackMLflowPrefectFeature storeDocker

First 30 daysTracked experiments and a repeatable train-eval-register flow behind a gate.

Deliverables

What you get.

Clear model outputs your business can evaluate and your technical team can operate.

Scope

Model brief

Business objective, target metric, input data, baseline, and acceptance criteria in one readable document.

Why it mattersEveryone agrees what success means before modeling starts.

Build

Production model package

A trained model with versioning, ownership notes, and the deployment path your team approves.

Why it mattersThe work moves beyond a notebook and into an operating service.

Control

Monitoring plan

Drift, quality, and performance checks written in business language with clear escalation rules.

Why it mattersYou know when the model is still trustworthy and when it needs attention.

Handover

Handover documentation

Retraining cadence, model assumptions, known limitations, and maintenance guidance.

Why it mattersYour team can operate the model without reverse-engineering our work.

By the numbersWhat we build first, and why

Forecasting: High value, contained effort - start here
Pricing: Highest value, real effort
Segmentation: Solid value, moderate effort
Dashboards: Quick, lower ceiling
GenAI pilot: Exploratory, effort before value

Stack we work in

From notebook to a monitored service.

Modeling

Pythonscikit-learnPyTorchXGBoost

Forecasting

ProphetstatsmodelsLightGBMNumPyro

MLOps

MLflowPrefectFeature storeDVC

Compute

DatabricksSparkRaySageMaker

Serving

FastAPIBentoMLTritonBatch

Monitoring

EvidentlyWhyLabsGrafana

How we work

From question to monitored model.

We keep the model work tied to one decision, one metric, and one operating path your team can own.

Phase 01
Framing
Define the decision the model informs and the metric that means success.
Time1 week
DeliverableProblem + eval spec
Phase 02
Baseline and data
A simple baseline and a clean feature pipeline before any fancy model.
Time1-2 weeks
DeliverableBaseline + features
Phase 03
Productionize
Pipeline, retraining, drift and eval gates, and serving behind an API or batch job.
TimeOngoing
DeliverableModel in production
Phase 04
Operate and hand off
Monitoring dashboards, model card, and runbooks. Your team owns it.
TimeMonth 4+
DeliverableOwnership handover