MLOps & LLMOps Pipelines
We build end-to-end ML and LLM operating fabrics, feature stores, training orchestration, drift monitoring and blue/green serving, so your models reach customers as fast as your code does.
The factory-floor that ships your models, every day.
A model is only as valuable as the velocity at which you can iterate on it. Our MLOps approach treats the path from notebook to production as a single, instrumented assembly line: features versioned, training reproducible, serving monitored and rollbacks one keystroke away. We build the same fabric for classical ML and modern LLM stacks, so your data scientists shape the science while the system handles the engineering.
Reproducibility by default
Every training run is hashable, code, data version, hyperparameters, seed. Replay last quarter's experiment in a single command.
Drift-aware, not drift-blind
Statistical and embedding drift monitors are wired in from day one. Retraining flows trigger automatically; humans approve the deploy.
Cost as a first-class metric
GPU and token spend are dashboards, not surprises. Autoscaling, model routing and quantization are part of the platform, not afterthoughts.
Why MLOps is the bottleneck, even when models work.
The hardest problem in enterprise AI right now isn't building the model. It's shipping it, monitoring itand replacing it without breaking everything downstream.
A 2025 industry survey put production-deployment rates below 1 in 8. The gap isn't science, it's the operational fabric around it.
In teams without proper MLOps, deploying a model takes longer than building it. Compounded across a roadmap, that's years of lost compounding value.
LLM workloads have rewritten ML cost economics. Without autoscaling, model routing and quantization, infra spend eats the ROI before it materializes.
MLOps & LLMOps Pipelines services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Feature stores with point-in-time correctness
No more train/serve skew. Features are authored once, served identically online and offline, with full lineage.
Reproducible training orchestration
Every run is hashable: code, data version, hyperparameters, seed. You can replay last quarter's experiment in one command.
Drift monitoring + auto-retraining
Statistical and embedding-based drift detectors trigger labelled retraining flows, never deploy a stale model again.
Blue/green & canary serving
Ship new models behind traffic-split policies. Roll back in seconds when business KPIs (not just accuracy) regress.
LLMOps: evals, traces, guardrails
For LLM stacks: prompt registries, golden-set evals, latency/cost dashboards and PII/jailbreak guardrails baked in.
GPU-aware autoscaling
Multi-tenant GPU clusters that pack workloads efficiently, cutting GPU spend without starving urgent jobs.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Cut model-deploy time from 6 weeks to 4 hours
Replaced a brittle Jenkins-and-S3 setup with a Kubeflow + MLflow pipeline. Now a data scientist ships to canary the same day they merge.
Six reasons enterprises run MLOps & LLMOps Pipelines with Infivit.
Built for the 2026 reality of MLOps & LLMOps Pipelines: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
Under 7 days from git push to live model.
Templated feature store, registry and CI/CD pipeline. Data scientists ship to canary the same day they merge, not the same quarter.
Canary, shadow and blue-green for any model.
Swap models, prompts, or LLMs with traffic-split policies. Roll back in seconds when a business KPI regresses, not just a loss curve.
Statistical and embedding drift in under an hour.
Detect data drift, concept drift and embedding drift before it shows up in your dashboards. Auto-trigger labelled retraining flows, every time.
Every prediction traceable to its inputs.
Code, data version, hyperparams, seed and weights are all hashed together. Replay any prediction your model made last quarter, in one command.
A/B and offline evals on every release.
No model ships without statistical significance. Golden datasets, regression suites and human-graded samples gate every deployment.
MLflow, Kubeflow, vLLM, BentoML, all yours.
Your pipeline outlives any vendor. Built on open standards so swapping orchestrators, registries, or serving layers is a refactor, not a rewrite.
The questions you were already going to ask.
Got a mlops & llmops pipelines problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.