ORVINUSMLOps & Model Serving
Training a model is a milestone. Serving it at 99.9% uptime, version after version, is the actual job.
MLOps and model serving is the engineering that turns a trained model into a dependable production service: inference endpoints with predictable latency, deployment pipelines that version, test, and roll back models the way disciplined teams handle code, and monitoring that catches degradation before your users do.
Most AI systems don't fail at training — they fail at operations. A model that works in a notebook meets reality as a service: concurrent requests, cold starts, version upgrades under live traffic, and the question of what happens at 3 a.m. when latency doubles. We build the serving layer and the pipeline around it to the same standard as the rest of our infrastructure work: 99.9% uptime through redundant deployment, health checks, and failover.
The same discipline extends to systems of models. Multi-agent orchestration — several models and tools coordinating on one workflow — is the pattern behind the workflow orchestration ORVINUS deployed for geekstudio.us, and it only works when the serving layer underneath is boring and reliable.
MLOps and model serving is the practice of deploying machine learning models as versioned, monitored production services. ORVINUS builds model serving and MLOps infrastructure including containerized inference endpoints, model registries with lineage, canary rollouts, evaluation gates in CI, neural network inference optimization, and multi-agent orchestration engineered for 99.9% uptime.
Model Serving & MLOps Pipelines
We deploy models as versioned, containerized services with the operational surface production demands: REST and streaming endpoints, request validation, health checks, and rollout strategies — canary or blue-green — that let a new model version take traffic gradually and roll back in one command when live metrics disagree with the offline evaluation. Serving code, model weights, and configuration travel together as one artifact, so what you tested is exactly what runs.
The MLOps pipeline treats models as deployable artifacts with lineage: a registry that traces every deployed version back to its training run and data, automated evaluation gates in CI so a model that regresses never ships, and reproducible packaging that removes the 'works on the data scientist's machine' class of incident entirely. Deployments become routine events your team runs weekly, not projects that consume a quarter.
Neural Network Inference Infrastructure
Inference infrastructure is where model economics are decided. We size and tune the serving stack against your actual traffic: CPU inference where it's cheaper and fast enough, GPU serving where latency or throughput demands it, dynamic batching that trades a few milliseconds for multiples of throughput, and quantization or smaller model tiers where the accuracy budget allows. Every choice is made by benchmark on your workload, not by habit or vendor default.
The result is a system you can reason about numerically: latency percentiles (p50/p95/p99) measured under load rather than quoted from a demo, per-request cost you can put in a spreadsheet, capacity plans derived from load tests, and scaling policies that add capacity ahead of the queue instead of after users notice. When traffic doubles, the answer to 'what happens?' is a document, not a discovery.
Multi-Agent Orchestration
Multi-agent orchestration coordinates several models, tools, and services into one reliable workflow: a planner that decomposes work, workers that execute steps, and a controller that owns retries, timeouts, and the exceptions that break naive pipelines. ORVINUS built exactly this for geekstudio.us — the backend infrastructure and workflow orchestration behind its multi-step processes — so the patterns here are deployed, not theoretical.
Reliability engineering carries the design. Every step is idempotent and resumable; workflow state is persisted so a crashed run continues from where it stopped instead of restarting; dead-letter queues capture the cases automation can't resolve and route them to humans; and full traces record every agent decision, so debugging a workflow reads like a log rather than an archaeology dig.
Built With
The technologies we reach for on this work — and why we use each one.
See what we use every tool for in the full technology stack.
What You Get
Serving layer
Your models as containerized, versioned services with health checks, streaming, and measured latency under load.
MLOps pipeline
Registry, evaluation gates, and CI/CD so shipping a new model version is a routine, reversible event.
Orchestration engine
Multi-agent workflows with persistent state, exception routing, and traces — where your use case calls for them.
Operations handover
Dashboards, runbooks, and documentation so your team operates the system without calling us — you own all of it.
Other AI Infrastructure Specializations
GPU Computing Pipelines
GPU-accelerated compute and real-time processing pipelines — throughput engineering for workloads CPUs can't finish on time.
Explore GPU PipelinesCloud Deployment & Scaling
Cloud-native deployment on AWS, GCP, or Azure — containers, auto-scaling, load balancing, and CI/CD engineered for 99.9% uptime.
Explore Cloud & ScalingBackend & API Engineering
High-performance backends, secure APIs, and database architecture — the service layer your product and AI systems stand on.
Explore Backend & APIsMonitoring & Observability
Logging, metrics, alerting, and AI model observability — the instrumentation that makes 99.9% uptime a practice, not a promise.
Explore Monitoring & AlertsWhere This Work Lands
SaaS & Startups
Ship an AI-powered product in weeks — on architecture that won't need rebuilding when you grow.
AI for SaaS & StartupsFinance & FinTech
Trading systems, quantitative research platforms, and financial AI built with institutional rigor.
AI for Finance & FinTechOperations & Automation
Multi-step workflows orchestrated by AI — with intelligent routing, error handling, and monitoring built in.
AI for Operations & AutomationCommon Questions
What is MLOps, in practical terms?
MLOps applies the discipline of software operations to models: versioning, automated testing, staged deployment, rollback, and monitoring. Practically, it's the difference between 'the data scientist deploys by SSH on Fridays' and 'a new model version ships through CI with an evaluation gate and rolls back in one command'.
Can you deploy models we've already trained?
Yes — that's the most common engagement. You bring the trained model; we build the serving infrastructure, registry, deployment pipeline, and monitoring around it. Discovery includes a review of the model's latency and resource profile so the infrastructure is sized to measurements, not guesses.
How do you roll out a new model version safely?
Staged exposure: the new version passes automated evaluation gates in CI, then takes a small slice of live traffic while we compare its metrics against the incumbent. If quality or latency regresses, rollback is one command. No big-bang cutovers, no deploy-and-pray.
Do we need Kubernetes for model serving?
Not always, and we'll say so. A single well-configured containerized service handles more traffic than most products ever see. Kubernetes earns its complexity at multi-service scale or strict availability requirements — we recommend it when the workload justifies it, and simpler container platforms when it doesn't.
Ready for MLOps & Serving?
Free discovery call — we scope the work, name the trade-offs, and respond within 24 hours.