Service 05

Data engineering & MLOps

The plumbing that decides whether an AI project survives its first quarter: reliable data in, versioned models out, and someone told when either breaks.

What it includes

Batch and streaming pipelines

Ingestion with schema contracts, backfill support and data-quality tests that fail the build, not the business.

Feature store

The same feature definition serves training and serving, which removes the single most common cause of silent model decay.

Model registry and promotion

Versioned models with a documented path from candidate to shadow to production, and a one-command rollback.

Drift and quality monitoring

Input distribution, output distribution and business KPI monitored together, because only the third tells you it matters.

Cost and latency dashboards

Per-model spend and p95 latency visible to the people who own the budget.

Reproducible training

Data snapshot, code commit and parameters pinned together so any result can be rebuilt months later.

What you receive

—Pipeline code with data-quality tests
—Feature and metric definitions
—Model registry and promotion process
—Monitoring and alerting setup
—Runbook for the operating team

Typical stack

dbt-style transforms
Airflow-style orchestration
Postgres / warehouse
Object storage
Grafana-style dashboards

·Batch and streaming pipeline design
·Model registry, evals and drift monitoring
·Cost and latency dashboards

Questions we are asked

We already have a data team. Where do you fit?: Usually on the serving and reliability side: contracts, feature parity, deployment rails and monitoring. We work inside your conventions rather than introducing a parallel stack.
Which warehouse do you work with?: Postgres, BigQuery, Snowflake, Redshift and lakehouse setups on object storage. We recommend based on your volume and existing licences, not a partnership.
How do you handle personal data in pipelines?: Minimise at ingestion, mask in non-production, restrict by role and log access. Retention rules are implemented in the pipeline, not left to policy documents.
What does drift monitoring actually alert on?: Input schema and distribution shifts, prediction distribution shifts and the downstream business metric. Alerts carry a suggested action so they are not just noise.

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