Vertex AI Partners

Picking use cases that carry a number

An initiative described as AI enablement produces demonstrations. An initiative described as reducing manual triage time on inbound service tickets by a stated percentage produces a system, because it can be measured, argued about and defended at budget time. The framing matters more than the algorithm. For each candidate, write down the decision or action the output will change, the person or process consuming it, the current baseline, and the improvement that would justify the build. Candidates that cannot survive those four lines are not ready. They may still be worth doing later, but they should not be the work that has to prove the platform.

The second filter is whether a model is the right instrument at all. A well-specified rule, a better-designed form or a lookup table sometimes beats a model on accuracy, latency and maintenance cost together. Where a model genuinely is warranted, sequence by feasibility against value: something valuable that depends on data you already collect cleanly should come before something more valuable that depends on data you do not yet capture. Getting one measurable result into production early buys the credibility and the operational learning that the harder second use case will need.

Data readiness questions to answer before modeling starts

• Does the label exist? — for supervised problems, whether you actually record the outcome you want to predict, historically and reliably, or whether it will have to be constructed.
• How far back does usable history go? — and whether a system change, a tracking migration or a pricing change makes the older portion unusable for training.
• Is the training-time view honest? — whether every feature would genuinely have been known at prediction time, since leakage from fields populated after the event produces excellent offline results and a useless deployed model.
• How will features be computed at serving time? — the same logic in the same way as during training, which is the problem a feature store exists to solve.
• What is the class balance and the cost asymmetry? — how rare the positive case is, and whether a false positive and a false negative cost the business the same thing.
• Who can approve use of this data? — whether personal or regulated fields are involved and what that permits for training, logging and retention.

What production adds to a model that already works

Between a notebook and a dependable service sits a body of work that rarely appears in an initial estimate. The training process has to become reproducible, which means parameterised code in version control, recorded data versions and logged experiment runs, so that the model in production can be rebuilt exactly. Feature computation has to be shared between training and serving so the two cannot drift apart. The model needs a deployment path with a rollback, an endpoint with a capacity and latency target, and a way of shifting traffic gradually between versions rather than replacing one with another and hoping.

Then there is the part the business sees. Somebody has to decide what happens when the model is unsure, when a dependency is unavailable, and when the prediction is plainly wrong — the fallback behavior is a product decision, not a technical detail. Predictions and the inputs that produced them need to be logged in a form you can query later, because without that you cannot investigate a complaint or measure real accuracy against outcomes. Ask any prospective partner to describe this layer in their proposal. Where it is missing, the estimate is for a prototype regardless of what the document calls it.

Serving decisions to make early

• Online or batch prediction — whether predictions are needed within a request cycle or can be computed on a schedule and written somewhere the application reads, which is dramatically cheaper.
• Latency and throughput targets — a stated millisecond budget and expected queries per second, because they determine machine type, accelerator choice and whether the model needs distilling.
• Endpoint autoscaling and minimum replicas — the trade between cold-start latency and paying for idle capacity overnight.
• Model size against cost — whether a smaller or distilled model reaches the accuracy the use case actually requires, since the largest model is rarely the most economical way to hit a threshold.
• Traffic splitting — the ability to send a small percentage to a new model version and compare outcomes before full promotion.
• Prediction logging — what is stored, for how long, and whether it is joinable to the eventual real-world outcome.

Pipelines, registry and retraining triggers

MLOps in practice is mostly about repeatability. A training pipeline expresses the whole sequence — data extraction, validation, feature engineering, training, evaluation, registration — as code that can be run on demand, on a schedule or in response to an event. The model registry is the inventory: each version recorded with the data it was trained on, the metrics it achieved, the evaluation it passed and its current deployment state. Without a registry, questions like which model is serving production traffic and what it scored on last quarter's holdout set become archaeology, and rolling back to a known-good version stops being a routine operation.

Retraining should be triggered by a condition, not by a calendar. Useful triggers include a measured drop in live accuracy once outcomes are known, a statistical shift in the input distribution beyond an agreed threshold, or the arrival of a defined volume of new labeled data. The pipeline should then retrain, evaluate against the incumbent on a fixed holdout, and refuse to promote a candidate that fails to beat it. Automatic promotion without that gate is how a quietly worse model reaches production. Getting this agreed before build is far cheaper than adding it after the first silent degradation.

Monitoring that produces an action

Monitoring an endpoint for availability tells you nothing about whether the predictions are still any good. Three signals matter. Input drift compares the live feature distribution with the training distribution and warns when the world has moved. Prediction drift watches the output distribution, which catches upstream breakages fast, because a feature silently arriving as null usually shows up as predictions collapsing toward a single value. Outcome-based accuracy compares predictions with what actually happened, and is the only true measure, though it arrives with a lag set by however long the outcome takes to materialise.

Each alert needs a named response before go-live. Who is paged, what they check first, what authority they have to route traffic back to the previous version, and at what point the model is disabled in favor of the fallback path. Write this as a short runbook and rehearse it once. The alternative is familiar: a dashboard full of drift charts that nobody owns, which produces either constant noise or complete silence, and a model whose degradation is first reported by the business rather than by the monitoring you paid for.

Who carries this after the engagement ends

• A named internal owner per model — with the access, the runbook and the authority to roll a version back without waiting for a change board.
• Retraining that your team can run — the pipeline triggered and inspected through the console or a command your engineers actually have, not a script on a consultant's machine.
• Budget for the running estate — endpoints, pipeline runs, storage and monitoring are an ongoing line item, and forecasting it is part of the handover.
• A decommissioning rule — the conditions under which a model is retired rather than maintained, so the estate does not accumulate services nobody uses but everyone pays for.

Evaluating outputs that have no single correct answer

Classification has an accuracy figure. A generated summary, reply or extraction does not, and teams that skip this problem end up shipping on the strength of a handful of impressive examples. The replacement for accuracy is an evaluation set: a few hundred real inputs with agreed criteria, scored consistently, and re-run whenever the prompt, the model version, the retrieval index or the temperature changes. Criteria should be specific to the task — factual grounding in the supplied source, correct extraction of named fields, appropriate refusal, adherence to required format and tone — and each scored independently rather than rolled into a single vague quality mark.

Scoring is part human, part automatic. Model-graded evaluation, where a separate model scores outputs against a rubric, is fast and scales to every change, but its agreement with your human reviewers has to be measured before you trust it. Deterministic checks catch a surprising amount on their own: valid JSON, required fields present, values within range, citations resolving to real sources. Insist that the evaluation set and the harness are delivered as your assets alongside the application. Without them you cannot safely change a prompt or adopt a newer model, which means you are locked to the configuration you launched with.

Guardrails to specify before launch

• Grounding and citation — whether answers must be supported by retrieved source material, and what the system does when retrieval returns nothing relevant.
• Refusal behavior — the categories of request the system declines, and what the user sees when it does, written as a specification rather than left to the model's defaults.
• Input and output filtering — safety filtering thresholds, plus checks for personal data leaking into prompts or logs.
• Prompt injection handling — how the system treats instructions embedded in retrieved documents or user-supplied content, which is the main attack path for anything reading external text.
• Cost and rate limits — token budgets per request and per user, since generative features fail expensively rather than loudly.
• Human review path — which outputs are reviewed before they reach a customer, and how reviewer corrections are captured for later evaluation.

Programs rarely stop at one product. Buyers hiring for Vertex AI often pair it with BigQuery partners , Campaign Manager 360 partners or Display & Video 360 partners , or review the whole Google Cloud landscape before committing.