What MLOps Services Cover
MLOps services cover the gap between a trained model and a served prediction. That means training compute, pipelines, a model registry, serving endpoints, monitoring, and retraining. We build that layer on Kubernetes and define all of it in Terraform.
Most ML projects stall on infrastructure, not modelling. GPUs get assigned by hand, every model ships a different way, and nobody is paged when accuracy falls. We run the ML platform like any other production system, using the same platform engineering and observability practice we apply to application workloads.
SquareOps is an infrastructure team first. Our engineers run production Kubernetes, data platforms, and DevOps pipelines for 500+ companies. That is the foundation MLOps needs. GPU capacity planning and cluster cost control are part of the build, not a later clean-up.
What's Included in Our MLOps Services
Grouped by phase: what we assess before building, what we build, and what we operate once models are live. Scope is set per engagement, not sold as a fixed package.
ML Infrastructure Assessment
We inventory your models, training jobs, data sources, and current compute. The output names the specific blockers between experiment and production, ranked by effort and impact.
Platform Architecture and Tool Selection
Cluster topology, storage for datasets and artifacts, network paths for inference, and a decision on Kubeflow, MLflow, Ray, or a managed service. We write down why each choice was made.
GPU Clusters on Kubernetes
GPU node groups on EKS, GKE, or AKS, running the NVIDIA GPU Operator and device plugin. Labels and taints keep non-GPU work off them. Karpenter provisions burst training capacity on demand.
In practice: the CIS-compliant, multi-AZ EKS platform we built for a SaaS provider.
ML Pipeline Automation
Training workflows orchestrated with Kubeflow Pipelines, Argo Workflows, or Airflow. Experiment tracking and a versioned model registry in MLflow, so every deployed model traces back to a run and a dataset.
Model Serving and Inference Endpoints
KServe, Triton Inference Server, Ray Serve, or TorchServe on Kubernetes. Request-based autoscaling, canary rollouts, shadow traffic, and a rollback path that does not need a rebuild.
Data and Feature Infrastructure
Training data stores, object storage layouts, dataset versioning with DVC, and feature serving with Feast. We run large MongoDB, Elasticsearch, and Postgres estates on Kubernetes for the teams that feed models.
In practice: 12 TB and 5 billion documents moved onto self-managed EKS with zero data loss.
LLM and RAG Serving Infrastructure
vLLM or Hugging Face TGI on GPU nodes, sized against context length and concurrency. Vector stores on pgvector, Weaviate, or OpenSearch, with retrieval services deployed beside the model.
Model Monitoring and Drift Detection
Inputs, predictions, and outcomes logged from every endpoint. Drift checks with Evidently, latency and error rates in Prometheus and Grafana, and thresholds that open a ticket or start a retraining run.
GPU Capacity and Cost Control
Spend attributed per team, model, and namespace. Spot capacity for checkpointed training, scale-to-zero for idle endpoints, and right-sized GPU types. Read our Karpenter autoscaling guide for the mechanics.
In practice: roughly 30% off a FinTech startup's AWS compute bill in four months.
CI/CD and GitOps for Models
Model artifacts promoted through the same pipeline as application code. Training triggered from GitHub Actions, GitLab CI, or Jenkins, deployments applied by Argo CD, every change reviewable in Git.
MLOps Challenges We Solve
Models stay in notebooks and never reach production
Data scientists deliver a trained model and a README. Nobody owns packaging, serving, or the cluster it needs, so the work stops at the handover.
Our Solution
We build one end-to-end path first: pipeline, registry, serving endpoint, monitoring. Once a model can ship through it, the next model is a config change rather than a project.
Training jobs wait for GPUs that are assigned by hand
One engineer keeps a spreadsheet of who has the GPU box this week. Experiments queue behind each other, and idle instances run overnight because nobody remembers to stop them.
Our Solution
GPU node groups with the NVIDIA device plugin, namespace quotas per team, and Karpenter provisioning on job submission. Capacity arrives when a job is queued and disappears when it finishes.
In practice: Tompkins Robotics cut environment onboarding from four weeks to under one.
Every model is deployed a different way
One model runs in a Flask container, another in a Lambda, a third on a data scientist's EC2 instance. There is no shared rollback, scaling, or logging behaviour.
Our Solution
A single serving standard on KServe or Triton, with autoscaling, canary rollouts, and structured logging applied to every model. Deployment differences move into configuration, not into new code.
Model accuracy degrades and nobody is alerted
The endpoint returns 200s, latency looks fine, and predictions have quietly got worse for two months. The business finds out before engineering does.
Our Solution
Prediction logging on every endpoint, drift checks with Evidently, and model quality panels in Grafana beside latency and errors. Thresholds page an owner or trigger a retraining run.
GPU spend grows without a named owner
Finance sees the GPU line rise every month. No tag maps an instance to a team or a model, so nobody can say which experiment caused the increase.
Our Solution
Cost attributed per namespace, team, and model, with spot for checkpointed training and scale-to-zero for idle endpoints. Cluster-level work follows our Kubernetes cost optimization practice.
In practice: BatchService cut data platform hosting 27%, from $30K to $22K a month.
Retraining is a manual process one engineer remembers
Refreshing a model means running notebook cells in the right order on the right laptop. When that engineer is on leave, the model goes stale.
Our Solution
Retraining defined as a pipeline in Git, triggered by schedule, new data, or a drift threshold. Runs are reproducible, artifacts are versioned, and promotion needs an approval instead of a login.
How an MLOps Engagement Works
Five stages from the first review to a platform your team operates. Where you join depends on what already runs.
Teams with an existing Kubernetes platform usually start at stage three. Teams with models but no cluster start at stage one, and the early work looks like an infrastructure audit.
ML Workflow Assessment
We map how models are trained, stored, and deployed today. Model inventory, data sources, GPU usage, and the manual steps between them are documented and ranked.
Platform Design
Cluster and GPU topology, artifact and dataset storage, serving network paths, and the toolchain decision. Designed as cloud-native architecture, reviewed with your ML team before any build.
Cluster and Pipeline Build
GPU node groups, the GPU Operator, and namespace quotas provisioned in Terraform. Kubeflow or Argo pipelines and an MLflow registry deployed and wired into your CI.
First Model in Production
One model taken end to end: packaged, deployed on KServe with autoscaling, released by canary. We roll it back once in a drill to prove the path works.
Monitoring and Handover
Drift and latency monitoring, retraining triggers, cost dashboards, and runbooks written for your environment. Your team operates it, or we run it under a retainer.
Ready to move your models into production?
Get a free ML infrastructure review covering your training compute, serving path, and GPU capacity.
Talk to an ML Platform EngineerMLOps Services by Workload Type
The infrastructure changes with the workload. These five shapes cover most of what we are asked to build.
Classical ML and Batch Scoring
Tabular models for scoring, forecasting, and ranking. CPU node pools, scheduled Argo or Airflow jobs, a feature store for consistent inputs, and results written back to your warehouse. The cheapest workload to run well and the easiest to leave unmonitored.
Deep Learning Training at Scale
Multi-GPU and multi-node training with PyTorch DDP, Ray Train, or Kubeflow Training Operator. Checkpointing to object storage so spot interruptions cost minutes, not a full run, plus queueing so teams share capacity without a spreadsheet.
Real-Time Inference APIs
Latency-bound endpoints behind your product. KServe or Triton with request-based autoscaling, warm pools to avoid cold starts, and SRE-grade error budgets and alerting applied to the serving path.
LLM and RAG Serving
vLLM or TGI on GPU nodes, sized against context length and concurrency rather than guesswork. Vector store, embedding jobs, and retrieval services deployed alongside, with GPU spend attributed per application.
Multi-Tenant ML Platforms
Several data science teams on one cluster. Namespace isolation, GPU quotas, self-service pipeline templates, and per-team cost reporting, built on the same internal developer platform patterns we use for application teams.
Kubernetes-Native MLOps vs Managed ML Platforms
The first decision in any MLOps engagement is whether to build on Kubernetes or adopt a cloud vendor's platform. Here is how the two compare on the points that decide it.
| Consideration | Kubernetes-native (Kubeflow, MLflow, KServe) | Amazon SageMaker | Google Vertex AI |
|---|---|---|---|
| Time to first model | Slower if no cluster exists | Fast, console-driven | Fast, console-driven |
| Portability | Runs on EKS, GKE, AKS, on-prem | AWS only | GCP only |
| GPU control | Direct: instance type, spot, quotas, sharing | Managed instance families | Managed instance families |
| Cost model | Raw compute plus your platform effort | Compute plus service premium | Compute plus service premium |
| Platform work required | Real: needs a platform team or a partner | Low | Low |
| Fits alongside your apps | Yes, same cluster and CI/CD | Separate service boundary | Separate service boundary |
| Best for | Teams already running Kubernetes at scale | AWS-only teams with no platform team | GCP-only teams with no platform team |
SageMaker or Vertex AI is the better choice for teams with two or three models and no platform engineers. The managed premium buys time you would otherwise spend on infrastructure. Kubernetes-native wins when GPU spend is material, workloads are already containerized, or a second cloud is on the roadmap. Mixed setups are common and fine: MLflow tracking on Kubernetes with SageMaker endpoints, for example.
