MLOps consulting for teams shipping classical ML and LLMs to production.
An mlops consultant practice that builds feature stores, serving infrastructure, continuous training pipelines, drift detection, and LLMOps observability. MLOps services for the gap between a model that scores well in evaluation and one that holds up at month six.
What MLOps consulting actually covers — six service areas.
MLOps consulting is the ops practice around models the team has already built. We don't write the model — that's the machine learning development sibling. We make the model survive production: feature stores, serving infra, continuous training, drift detection, registry, and LLMOps when the workload is generative. The first call is usually about which two of the six gaps below hurt most.
Most mlops services engagements start with two of these six and grow from there. The audit phase in section seven picks the order.
MLOps services — what we build and operate, tool by tool.
Per-service tool picks with the trigger conditions written down. The mlops services we ship are picked from this matrix per engagement — never the whole list at once, and never "all OSS" or "all managed" as a blanket call. Each row carries when we pick it, when we don't, and the Paiteq default.
vLLM for any LLM above ~50 req/s — PagedAttention plus continuous batching halves GPU spend versus naive HuggingFace TGI. Triton when one cluster serves vision, tabular, and LLM together. BentoML wraps both for Python-first deploy.
Latency budget with a stated p95; mixed model families on shared GPUs; team can run a serving stack 24/7.
Single low-volume model where FastAPI behind a load balancer is the right shape and Triton's operational tax doesn't pay back.
vLLM on Kubernetes with KEDA scaling on queue depth. Most mlops services engagements start by replacing a fragile Flask wrapper with this stack.
Feast handles the 90% case at 10% of Tecton's operational cost — Feast plus Redis online plus BigQuery or Snowflake offline is the most common shape we ship. Tecton is overkill for teams under 50 features in production. Hopsworks when point-in-time correctness has to be audited.
Two or more models share features; training-serving skew has bitten the team at least once; data scientists keep rebuilding the same features in Jupyter.
Single model with five features in one SQL view — the store adds operational surface that doesn't pay for itself yet.
Feast first, Tecton only when feature freshness goes below 60 seconds. Most feature store implementation engagements land at Feast plus Redis plus a thin schema-registry.
MLflow is the boring right answer — every cloud has a managed flavour, the OSS runs on a single VM, registry plus experiment tracker plus artifact store from one library. DVC adds data lineage: artefact knows its dataset version, dataset knows its raw extract.
More than two engineers on the team; any production model where rollback matters; any regulated workload where lineage has to be auditable.
Single-engineer shop where a Git tag plus a Postgres row is the registry — sometimes that's enough for the first six months.
MLflow plus DVC plus signed model cards in Git. Every artefact carries eval scores, training data hash, and the engineer who promoted it.
Kubeflow Pipelines for K8s-native teams that want vendor portability. Vertex AI Pipelines on GCP and SageMaker Pipelines on AWS when skipping cluster ops dwarfs the lock-in. Airflow when the data team already runs it and ML-versus-data orchestration is blurry.
Kubeflow when ML platform is its own product surface; Vertex or SageMaker when ML is one workload among many and platform engineers are scarce.
Nightly batch retraining on a single tabular model — a cron job plus MLflow runs is honest and ships in a week.
Vertex AI or SageMaker Pipelines for the first ML platform; Kubeflow when the team grows past three engineers and the lock-in starts hurting.
Langfuse covers tracing, prompt versioning, evaluator runs, and dataset management in one OSS surface — the closest thing to a default. Helicone is cleaner when the team just wants a proxy in front of OpenAI or Anthropic with cost telemetry. Phoenix Arize for hallucination scoring and embedding drift.
Any LLM in production; any team with a per-token bill growing month-on-month; any product where prompt regression has caused an incident.
Single internal chat assistant under a hundred requests a day — instrumentation overhead outpaces the spend.
Langfuse self-hosted alongside the app, Helicone proxy in front of the provider for failover and per-tenant caps. The stack pays for itself before week four in most engagements we've audited.
Evidently AI is the OSS workhorse — PSI, KL divergence, Wasserstein on inputs, plus prediction drift and ground-truth concept drift when labels arrive. NannyML when the team needs estimated performance without labels. WhyLabs or Arize as managed alternatives.
Any model in production longer than 60 days; any model whose inputs shift faster than retraining cadence; any regulated workload where missed degradation is a compliance event.
Static-input batch model retrained nightly on a sliding window — the retrain pace effectively monitors itself.
Evidently AI wired to PagerDuty via a thin Python alert router. PSI 0.15 inspect, 0.25 retrain trigger — adjusted per-feature after the first month of baseline.
ML platform engineering — three continuous training patterns.
CD4ML — continuous delivery for machine learning — comes in three flavours in 2026: scheduled batch, drift-triggered, and streaming. Roughly half the ml platform engineering engagements we audit need to move from one tier to the next, not jump straight to the most expensive shape. The pattern carousel below names what each one wins and where it breaks.
Scheduled batch CT
The simplest CT pattern. A Kubeflow or Vertex AI Pipeline fires on a schedule, pulls the last N days of labelled data, validates with Great Expectations, retrains, evaluates against a frozen held-out set, and promotes if eval passes. DVC pins the dataset version. MLflow logs lineage on every run. The right starting shape for any team without drift instrumentation in place — schedule first, drift-triggered later. Most ml platform engineering engagements start here in week three.
- Stable-input workload where drift creeps slowly
- ground-truth labels arrive on a known cadence
- team is new to MLOps and needs a working CT loop before they instrument drift
- tabular boosters or recommendation models on a daily feedback loop
- Fast-shifting input distribution (fraud, ads bidding)
- LLM workloads where prompt drift and eval drift outpace any retraining schedule
- environments where retraining cost is a meaningful slice of the inference bill
Drift-triggered CT
Evidently AI computes PSI on every input feature on a rolling window. When PSI crosses 0.25 on any feature or prediction drift crosses its threshold, the pipeline kicks off — pull fresh data, validate, train, shadow-evaluate, promote if gates pass. The cron job becomes a safety net, not the primary signal. Cheaper than nightly retraining for stable models, sharper than nightly for unstable ones. This is the shape the mlops consultant work usually moves teams to by month two.
- Input distribution shifts irregularly
- retraining cost matters
- team has the drift instrumentation in place to trust the trigger
- model has a clean rollback path so an automated promotion can be reverted in under five minutes
- Brand-new model with no production baseline yet — drift threshold isn't calibrated, you'll fire false-positive retrains
- environments without a fast rollback contract
Streaming feature + near-real-time CT
Kafka or Pulsar streams events into Feast's online store via a stream ingestor. The serving layer reads features at request time with sub-100ms p99. The retraining pipeline polls every 15-30 minutes, retrains an incremental checkpoint, and shadow-evaluates against a sliding holdout. This is what fraud, ads bidding, and dynamic pricing models look like in 2026 — and it's the most expensive pattern to ship and operate. We don't recommend it until the team has lived through patterns 1 and 2 first.
- Hard freshness SLA under 15 minutes
- revenue-sensitive workload where stale features cost real money inside the hour
- team has SRE depth to run a streaming feature pipeline plus a continuous training loop without on-call burnout
- Anything that can tolerate hourly or daily features
- teams under three ML infra engineers
- cost-constrained environments where the streaming bill outpaces the model's economic lift
LLMOps — what changes when the model is a frontier LLM.
Classical MLOps was built around drift on tabular features and a once-a-week retraining cadence. LLMOps inverts every assumption. The drift signal is an evaluator score, not a distribution distance. The cost lever isn't retraining frequency, it's model routing and semantic cache. The promotion gate isn't a held-out metric, it's a judge-graded eval suite. We handle both in the same engagement — but the runbooks, the dashboards, and the failure modes are different practices.
| Classical MLOps | LLMOps | |
|---|---|---|
| Primary failure mode | Distribution drift on tabular features — silent precision/recall decay over weeks | Prompt-version regression, hallucination rate spikes, eval drift on judge-graded outputs — fast and noisy |
| Classical MLOps failures are slower-moving and more predictable — a drifting recommender degrades over days, giving the monitoring stack time to catch it. LLMOps failures can land in production within a single deployment; a bad prompt version ships bad outputs immediately with no statistical lag to hide behind. | ||
| Drift signal | PSI / KL divergence on input feature distributions; ground-truth concept drift when labels arrive | LLM-as-judge eval scores on sampled production outputs; guardrail hit rate; per-prompt regression deltas |
| LLM-as-judge eval is measurable same-day — no waiting on human-labelled ground truth. Classical PSI/KL signals are statistically rigorous but depend on label availability that can lag weeks; the signal is more trustworthy once it arrives, but slower to arrive. | ||
| Cost lever | Retraining frequency, GPU vs CPU serving, batch vs online inference | Model routing (Sonnet vs Opus vs Haiku), semantic cache hit rate, prompt-length budgeting, batch API for non-urgent |
| Observability stack | Prometheus + Grafana for serving metrics; Evidently AI for drift; MLflow for run history | Langfuse for traces and eval runs; Helicone for cost telemetry; Phoenix Arize for embedding drift |
| The classical stack (Prometheus + Grafana + MLflow) is battle-tested at scale with years of production hardening and broad community tooling. LLMOps tooling is maturing fast but the ecosystem is still fragmented — Langfuse, Helicone, and Phoenix serve different slices with no unified pane yet. | ||
| Retrain trigger | Drift threshold breach OR scheduled cadence; days-to-detect varies by label availability | Eval regression on the gold prompt set; same-day detection if the evaluator runs nightly |
| Promotion gate | Held-out eval beats champion by agreed margin; calibration check; slice-fairness check | LLM-as-judge eval suite passes; hallucination rate below threshold; guardrail hit rate stable |
| Classical held-out eval against a fixed test set is a more objective, reproducible gate — a numeric margin is deterministic. LLM-as-judge promotion gates introduce evaluator variance; the judge model itself can drift, so the gate requires periodic re-anchoring against human spot-checks. | ||
The LLMOps section is the differentiation gap most mlops consulting providers leave open — classical MLOps content is everywhere, LLMOps-specific runbooks are not. If your stack is LLM-heavy, this is the conversation worth starting with.
Continuous training pipelines — four eval-gated phases.
CD4ML is a named practice, not a vibe. Every retrain that reaches production passes four gates in order — data validation, retrain trigger logged with cause, shadow eval against the champion, automated promotion with a blue/green rollback contract. Skip a gate and you're back to manual retraining with a postmortem at the end. We won't ship a pipeline that doesn't carry all four.
Data validation
Every batch entering the training pipeline runs through Great Expectations or Soda Core checks — schema, null fractions, range, distribution sanity. Validation failures halt the pipeline before a single GPU minute burns. Dataset version pinned via DVC; the model artefact will know exactly which slice trained it.
Retraining trigger
Either the drift detector (Evidently AI PSI breach over a per-feature threshold) or the schedule (whichever fires first) kicks the Kubeflow or Vertex AI Pipeline. Trigger condition logged with the run; you can read why any retrain happened six months later in MLflow.
Shadow evaluation
The candidate model runs alongside the champion against the frozen held-out set and a sampled production stream. Eval gates: held-out metric beats champion by the agreed margin, calibration error stable, slice-fairness across the protected dimensions doesn't regress. No gate, no promotion.
Automated promotion
Blue/green traffic split — 10%, 50%, 100% — with a rollback gate at each step keyed to production metric guardrails. If the live precision-recall slips during the 10% phase, traffic reverts to the champion in under five minutes. The runbook names who gets paged and what they do.
Gate four is the one teams under-invest in. The promotion contract has to roll back inside the SLA — we test that monthly on a calendar invite, not as a tabletop exercise.
ML model monitoring — four drift signals on every production model.
ML model monitoring isn't a single metric; it's four signals layered on the same model, each with its own detection lag and its own decisiveness. Data drift moves first, prediction drift moves next, concept drift confirms the call, and for LLM workloads the evaluator score moves fastest of all. We instrument all four because no single one is sufficient — and the threshold table below is the default calibration before the first month of baseline data adjusts it.
- 01 Data driftPSI < 0.15
Evidently AI computes Population Stability Index per input feature on a 7-day rolling window vs training distribution; KL divergence as a cross-check on continuous features.
PSI > 0.25 fires retrain trigger; > 0.4 pauses automated decisioning if the use case requires it.
- 02 Prediction driftDistribution stable
Wasserstein distance on the model's output distribution on a 24-hour rolling window. Catches degradation before ground-truth precision/recall arrives — usually weeks before the labels confirm it.
Distribution shift > agreed band routes to on-call and flags the champion for manual review.
- 03 Concept driftHeld-out metric stable
Ground-truth labels collected on a sampled production stream; retrospective eval against the original held-out metric on a 30-day window. The slowest signal but the most decisive one — concept drift means the world changed, not just the inputs.
Held-out metric below threshold triggers an audit memo and a retrain decision in writing.
- 04 LLM eval driftJudge score within band
Langfuse evaluator runs every night on a sampled batch of production completions, graded by an LLM-as-judge against the gold prompt set. Hallucination scoring, instruction-following, guardrail hit rate all tracked per prompt version.
Eval score below the band rolls back to the previous prompt or model version; same-day detection cadence.
Drift threshold reference — defaults we ship with.
Per-feature thresholds calibrate after the first month on real history. The defaults below are the starting points for a model with a clean baseline and no exotic seasonality. Where they break, the audit memo names the per-feature adjustment in writing.
| Signal | Tool | Inspect at | Retrain at | Pause at |
|---|---|---|---|---|
| Categorical feature PSI | Evidently AI | 0.10 | 0.25 | 0.40 |
| Continuous feature KL | Evidently AI | 0.05 | 0.15 | 0.30 |
| Prediction distribution Wasserstein | Evidently AI | Per-model band | 1.5× band | 3× band |
| LLM judge score (1-5) | Langfuse | -0.2 vs gold | -0.4 vs gold | -0.7 vs gold |
| Guardrail hit rate | Helicone | +30% week-on-week | +60% | +100% |
| Hallucination rate | Phoenix Arize | +1pp vs baseline | +3pp | +5pp |
How an mlops consultant engagement actually runs.
Four phases, twelve weeks for a typical end-to-end engagement, fixed-scope per phase. The audit ships an opinionated memo before the build starts; the build phase ships a working CT pipeline; the monitoring integration overlaps the back half of the build so the dashboards are live before the team gets handed the on-call rota. We don't sell open-ended retainers in the build phase — operate-the-platform contracts come later if the client wants them.
Failure-mode catalogue, current-stack read, prioritised gap list with cost estimates
Audit memo signed; the three highest-leverage gaps named in writing.
Feature store, serving infra, model registry, CI/CT pipeline scaffolding in your repo
First end-to-end pipeline run lands a model in registry behind a feature flag.
Evidently AI plus Langfuse plus Helicone wired in; alert thresholds calibrated on real history
First drift breach detected in a calibrated false-positive band on production traffic.
On-call runbooks, retraining playbooks, dashboard tour, named-owner rota
Client team runs an end-to-end retrain unsupervised; we step off the on-call rota.
An AI readiness and infrastructure audit is often the right starting shape if the team isn't sure yet whether MLOps is the gap — we'll route there if the audit memo says so.
Typical engagement shapes — three patterns we see most.
Three engagement archetypes by deliverable and segment. Outcome framing is qualitative — we don't carry borrowed metrics from other practices, and the mlops consultant work in this practice ships fresh per engagement.
Drift-triggered CT pipeline build-out
A Kubeflow or Vertex AI Pipelines flow that retrains on PSI breach, validates with Great Expectations, runs shadow eval against the champion, and promotes through a blue/green gate. Feast online + offline store ships alongside. Typical shape: the team moves from nightly batch retrain to drift-triggered inside the build window, and the rollback contract becomes one command instead of a 30-minute incident.
Drift detection rollout across a model portfolio
Evidently AI monitors per model — data drift, prediction drift, concept drift on lag-arriving labels. PagerDuty routing with a named owner per model. Retrospective ground-truth eval cadence calibrated to label arrival. Typical shape: the silent-degradation gap that used to surface at quarterly review closes inside SLA, and the regulator's audit memo stops being a manual exercise.
LLMOps stand-up for an existing GenAI feature
Langfuse self-hosted for tracing, prompt versioning, evaluator runs. Helicone proxy in front of the provider for cost telemetry, per-tenant caps, and failover. LLM-as-judge nightly eval against a gold prompt set. Typical shape: prompt-version regressions stop reaching production silently, and per-tenant cost ceilings live as code rather than a quarterly Slack scramble.
Outcomes are framed as deliverable and shape because Paiteq's MLOps practice ships per engagement, not against a borrowed-stat library. The audit phase is where the specific success criteria get named in writing.
MLOps versus managed ML platforms — when to build, when to use Vertex AI.
The single most expensive misframing in this category is teams building a Kubeflow platform when Vertex AI Pipelines plus a Paiteq advisory retainer would have shipped in a quarter of the time. The inverse exists too — teams stuck on a managed platform when feature freshness contracts have outgrown what the managed service can deliver. The decision tree below is the screen we run on every inbound. Cross-link: our model development and training practice covers the model build itself.
Three questions. Three to four terminal recommendations.
Why teams pick our mlops consulting services — three honest reasons.
-
01 Named tools, not "best-of-breed"
vLLM, Feast, MLflow, Evidently AI, Langfuse, Helicone, Kubeflow, Vertex AI Pipelines — named in writing in the audit memo with trigger conditions for when we pick each. We won't sell you "a leading feature store" or "a state-of-the-art observability platform" — every tool comes with a when-we-pick and when-we-don't.
-
02 LLMOps in the same engagement
Most mlops consulting providers stop at classical MLOps and hand the LLMOps work to a separate vendor. We don't. Langfuse, Helicone, Phoenix Arize, prompt-version regression detection, LLM-as-judge eval cadence — all in the same engagement, instrumented against the same dashboard surface.
-
03 Audit memo before the build
The audit phase is two weeks fixed-scope and ships an opinionated written memo — the three highest-leverage gaps, the costed roadmap, the recommendation on build-vs-managed. If the memo says you don't need the build yet, we'll say so. About one engagement in seven ends at the memo, and that's the right outcome.
What buyers ask before signing an MLOps engagement.
What's the difference between MLOps and LLMOps, and do you handle both?
Same operational job, different failure modes. Classical MLOps consulting work is mostly about feature freshness, training-serving skew, and distribution drift on tabular features — the model degrades slowly and the drift signal is a PSI or KL divergence. LLMOps is about prompt-version regression, hallucination rate, guardrail hit rate, and eval drift on judge-graded outputs — the model degrades fast and the signal is an evaluator score, not a distribution distance. We handle both in the same engagement when the team's running a hybrid stack, which is most of them in 2026. Cross-link: LLM application development covers the build side; this practice covers the ops side after the build ships.
How long does it take to set up a CI/CT pipeline for an existing ML model?
For a model with a clean training script and a held-out eval set, the first end-to-end CT pipeline lands inside the platform-build window — usually weeks two through eight. The audit phase comes first to read the existing stack, name the highest-leverage gaps, and pick the orchestrator (Kubeflow, Vertex AI Pipelines, or SageMaker Pipelines). Drift instrumentation lands in weeks six through ten so the trigger condition is calibrated on real history, not a guessed threshold. We don't ship a pipeline that retrains on noise — the false-positive rate gets calibrated before the trigger goes live.
Can you work with our existing cloud — AWS SageMaker, GCP Vertex AI, or Azure ML?
Yes. We start by reading what's there, not by replacing it. Vertex AI Pipelines plus Feast plus Evidently AI on GCP; SageMaker Pipelines plus Feast plus Evidently AI on AWS; Azure ML plus MLflow plus Evidently AI on Azure. Kubeflow on top of EKS, GKE, or AKS when the team wants vendor portability and has the platform engineers to run it. We've seen too many engagements derailed by a premature lift-and-shift — fix the gaps in the current stack first, then have the portability conversation in year two with real production data behind it.
How do you detect model drift before it impacts production metrics?
Three layers. Input drift via Evidently AI computing PSI and KL divergence per feature on a rolling window — usually catches a shift two to three weeks before precision and recall move. Prediction drift via Wasserstein distance on the model's output distribution — catches degradation before ground-truth labels arrive. Concept drift via retrospective eval on lag-arriving labels — the slowest signal but the most decisive. For LLMs we add a fourth: Langfuse evaluator runs nightly against a gold prompt set, with hallucination rate and guardrail hit rate tracked per prompt version. The thresholds in section six are the defaults we calibrate after the first month.
What does an MLOps engagement cost, and how is it scoped?
Scoped per-phase, not per-month-retainer. The audit is two weeks fixed scope; the platform build is six to ten weeks depending on the orchestrator and feature store choice; monitoring integration overlaps the back half of the build; handoff is two weeks of runbook work and on-call shadowing. Pricing is fixed-scope per phase with a quoted total at audit signoff — no open-ended retainer baked in. An operate-the-platform retainer after handoff is a separate contract so the build-phase deliverables stay unambiguous.
Ship an MLOps platform in twelve weeks.
Audit in 2 weeks. Platform build in 6-10. Monitoring integration overlaps. Handoff with runbooks.