Professional Services · ML Pipelines
Pipelines that retrain themselves before drift breaks production
Production ML is a loop, not a launch. We engineer the data, training, deploy, and monitor loops so your models retrain when the world moves, promote on signed evidence, and roll back automatically when a regression hits canary.
Representative pipeline
Daily retrainFraud detection · 14 features · 4 models in registry
Ingest
Streaming + batch
Features
Online + offline store
Train
Distributed run
Eval
vs eval set + canary
Deploy
Registered, signed
Monitor
Drift + perf alerts
Pipeline-as-code in your repo. Lineage tracked. Drift on the Monitor step triggers retraining on Train . The loop is the product.
The loops we engineer
Production ML is closed-loop
Data, training, deploy, monitor. Each loop has its own cadence and its own failure mode. We engineer all four so they trigger each other.
Data loop
Ingest → Features → Validate
Streaming and batch ingest into the feature store. Schema validation, freshness SLAs, lineage from source to feature. The loop your downstream training pipeline can actually trust.
Artefacts
Connector pack · feature definitions · contract tests
Training loop
Trigger → Train → Eval → Register
Time-based, drift-based, or business-event-based triggers. Distributed training with checkpointing. Evaluation against held-out and canary slices. Registry promotion on a signed-off decision.
Artefacts
Training DAG · eval suite · registry policy
Deploy loop
Register → Stage → Canary → Roll
Versioned model artefact through staged environments. Canary against a slice of live traffic. Auto-rollback on regression. Same pattern whether you serve through your own gateway or our inference cluster.
Artefacts
Deployment workflow · rollout policy · rollback hook
Monitor loop
Observe → Detect → Trigger
Data drift, concept drift, prediction quality, and downstream business metric. Alerts that name the cause, not just the symptom. When threshold breaks, the data + training loops re-run.
Artefacts
Drift dashboards · alert routing · retrain triggers
Where pipelines quietly rot
The failure modes we've already automated around
Most ML estates don't break loud. They drift slowly until the metric nobody was watching crosses a line.
Static model in a moving world
What rot looks like
Quarterly manual retrain
What we ship
Drift-triggered retrain on the same pipeline
Most ML breakage isn't a code bug. It's a model trained on last year's distribution serving this quarter's traffic. The fix isn't a calendar reminder; it's a retrain pipeline that runs when the distribution moves, every time.
Lineage you can't reconstruct
What rot looks like
Which features did v3 use?
What we ship
Feature → run → model → prediction trace
When a regulator or a debugging session asks what the v3 model saw, the answer needs to come from the system, not from someone's memory. Lineage from source data through to live prediction, captured by the pipeline, not bolted on after.
Manual deploys that nobody remembers how to do
What rot looks like
Slack the senior to push
What we ship
Promotion is a state-machine transition
Production ML that depends on tribal knowledge stops shipping the day that person takes leave. Promotion through stages becomes a state-machine transition with signoffs, gates, and rollback.
Drift alerts that never resolve
What rot looks like
Page everyone every Monday
What we ship
Causal alerts + retrain on threshold
An alert that fires but has no automated response is just noise. Drift detection needs to be wired into the retrain trigger, not into a Slack channel nobody reads.
Tooling we drive
We pick the stack that fits your runtime + team
No religion. The right stack is whichever one your team can run on a Sunday without paging us.
Orchestrators
Airflow · Kubeflow Pipelines · Flyte · Prefect · Dagster · Argo Workflows · Metaflow
We have production experience across these orchestrators. The right pick depends on your team's comfort, your runtime, and the granularity of unit you want to schedule.
Feature stores
Feast · Tecton · Hopsworks · in-house on top of your warehouse
Online and offline parity is the hard part. We pick for whichever closes that gap with the smallest amount of glue code in your stack.
Model registries
MLflow · Weights & Biases · cloud-native (Vertex / Sagemaker / Azure ML)
Versioned artefacts, signed promotions, audit trail. The registry is the single source of truth that production deploys read from.
Drift + monitoring
Evidently · WhyLabs · Arize · Fiddler · custom on Prometheus / Grafana
Statistical drift on inputs, prediction quality on outputs, business-metric drift downstream. The three together; one alone is not enough.
Runtimes
Kubernetes (any flavour) · serverless · managed cloud MLOps · Yobitel-hosted
The pipeline definition stays portable. We build to your runtime, not the other way around.
Your handover pack
What lands at sign-off
Concrete artefacts that make the pipeline estate runnable by your team without us. No bus-factor of one.
Pipeline-as-code repository
Every pipeline as versioned code in your repo. Reviewable, testable, rollback-able. No clickops in a UI nobody remembers signing into.
Feature catalogue + contracts
Every feature has a definition, an owner, a freshness SLA, and a test that fails loud when an upstream change breaks it.
Registry policy doc
How a model graduates from candidate to staging to production. Who signs off, on what evidence, with what automatic gates.
Drift detection + retrain wiring
The alerts, the thresholds, and the automatic retrain trigger that closes the loop without anyone paging on a Sunday.
Lineage dashboard
Source data → feature → training run → registered model → live prediction. Searchable, queryable, auditable.
Runbook for failed runs
When a pipeline run fails at 3am, what happens. Who is paged, what the first-line response is, when escalation kicks in.
How we engage
Pick the shape that fits your team
Yobitel-led
We build and run the pipelines
Discovery through running pipelines plus optional day-2 ops handover. Best for teams that don't have a dedicated ML platform function yet.
Collaborative
We pair with your platform team
We bring the patterns and the rougher edges (drift detection, feature contracts, lineage); your team owns delivery and runs it after.
Advisory
Time-boxed review
Audit your existing pipeline estate. Where the breakage will come from. What to fix first. Delivered as a written report and a workshop.
Tell us what your pipelines should do.
A short questionnaire covers workload, platform, and engagement model. Our pipelines practice lead replies inside one working day with a topology, a tooling pick, and a timeline to first running pipeline.
Same engineering bench that handles the training cluster and the inference fleet. Engagements scoped to any sovereignty perimeter. Optional 24/7 day-2 handover. Pipeline-as-code in your repo from day one. Drift-triggered retrain built in.