AI + Cloud &
MLOps Services
The gap between a notebook that works and a model that earns its keep in production is enormous. We build the pipelines, serving, and monitoring that carry your models and LLMs from experiment to reliable, observable, cost-aware production, and keep them healthy there.
The Machinery Behind Every Model.
Production ML is an operational discipline, not a single tool. These are the building blocks we stand up and run so your data science stops being a set of one-off notebooks and becomes a repeatable, governed system.
Feature Stores
One governed source of truth for features, consistent between training and serving so online and offline never drift apart.
Experiment Tracking
Every run, dataset, and hyperparameter logged and reproducible, so a promising result can always be rebuilt, not just remembered.
Model Registry
Versioned, stage-gated models with approvals and provenance. Promotion from staging to production is a controlled, audited event.
Serving & Inference
Low-latency real-time endpoints and high-throughput batch jobs, autoscaled on CPU or GPU with the right accelerator per workload.
Drift & Quality Monitoring
Data drift, concept drift, and live accuracy tracked against baselines, so silent model decay is caught before it costs you.
CI/CD + Continuous Training
Pipelines that test, validate, and retrain on fresh data automatically, promoting a new model only when it beats the incumbent.
A Model Is Never Done.
Shipping a model is the start, not the finish. Production reality shifts underneath it, so the lifecycle closes back on itself: monitoring feeds retraining, and every loop makes the next model better than the last.
Data & Features
Ingest, validate, version data and materialize features.
Train & Tune
Train with tracked experiments on scalable compute.
Evaluate
Score on holdouts, fairness, and the live champion.
Deploy
Package, register, roll out behind canary guardrails.
Monitor
Track drift, latency, and accuracy in production.
New Models Earn Their Traffic.
A model that looks great offline can still misbehave on live traffic. We never flip the whole fleet at once, every release proves itself on a slice first, with the guardrails that pull it back the moment a metric slips.
Shadow
Mirror live traffic to the new model with zero user impact, and compare outputs offline.
Canary
Route a small slice of real traffic first, widening only while the metrics stay green.
Blue-Green
Stand the new version up alongside the old, switch instantly, and roll back just as fast.
A/B Test
Split users across versions and let a real business metric decide the winner.
Models Rot Quietly.
A model is only as fresh as the world it was trained on, and the world keeps moving. We watch for the three ways it goes stale and trigger a retrain before the decline ever reaches your users.
Data drift
The input distribution moves, new behavior, seasonality, or a changed upstream source.
Concept drift
The link between inputs and the target shifts, yesterday's signal stops predicting.
Performance decay
Live accuracy quietly slides even while the inputs still look perfectly normal.
LLMs In Production, Grounded & Governed.
Generative AI brings its own operational problems, hallucination, prompt sprawl, runaway token bills, and safety. We wrap your LLM apps in a serving pipeline that keeps answers grounded in your data and every call measured.
Prompt
Versioned system + user prompt
Retrieval
Vector search over your data
Model
Routed LLM, semantic cache
Guardrails
PII, safety, hallucination checks
Response
Grounded answer with citations
Expensive Silicon, Fully Used.
GPUs are the most costly thing on your ML bill and the easiest to waste. We treat accelerator time as a first-class FinOps problem, so utilization stays high and idle capacity stops quietly draining the budget.
Right accelerator per job
Big GPUs for training, smaller or CPU for inference, matched to the workload not the habit.
Spot for interruptible training
Checkpointed jobs run on spot capacity with automatic resume, at a fraction of on-demand.
Sharing & MIG partitioning
Slice a GPU across small models so nothing sits half-idle burning money.
Scale inference to zero
Endpoints spin down when idle and cold-start fast, so you pay for traffic, not for waiting.
Past The Demo, Into Production.
Most models never make it out of the notebook, and the ones that do often break quietly. We close that last, hardest mile, and we bring the operational discipline that keeps them working long after launch day.
Research meets ops
We are ML platform engineers, equally at home in PyTorch and in Kubernetes, so nothing gets lost in the handoff.
one teamReproducible by default
Every model traces back to its exact data, code, and parameters. A result is never something you just have to trust.
full lineageCost-aware from day one
GPU and inference spend is designed down as you build, not discovered as a shock on next month's invoice.
FinOps built inOpen, not locked in
Open standards and your cloud of choice. The platform we build stays yours, portable, and free of proprietary traps.
no lock-inFrequently Asked Questions.
Still have questions?
Our ML platform and MLOps engineers are happy to talk specifics.
Talk to an ExpertNo. We can be your entire ML platform and operations function, or slot in alongside the data scientists you already have and take the engineering and operational load off them. The goal is the same either way: get good models into production reliably, without your specialists spending their days on infrastructure plumbing.
From Notebook To Production
Bring us a model that works on your laptop and a goal for production. We will map the path to get it there reliably, and show you exactly what a production-grade ML platform looks like for your stack.