Data Science Teams Stuck in Notebooks
Your models work on a laptop, but getting one into production takes months of hand-offs. We turn notebooks into pipelines and give your data scientists a self-service path to a served endpoint.
Get Your Models
out of the Notebook.
Training pipelines, model serving and GPU infrastructure on Kubernetes — so models reach production and stay reliable there.
Trusted across Europe
Industries we serve.
Engineering teams in regulated, mission-critical industries — every engagement audited, documented, and production-grade.
Banking & Payments
FinTech
PCI-DSS compliant payments and core banking infrastructure — sub-100ms p99 latency, end-to-end audit trail, and tokenization at the edge.
PCI-DSS · ISO 27001
Patient Data
Healthcare
HIPAA-aware patient data pipelines
HIPAA · SOC2
5G & Networks
Telecom
5G core network observability at scale
NFV · ETSI MANO
Retail & Marketplaces
E-Commerce
99.99% uptime during peak traffic events
PCI-DSS · GDPR
Sovereign & Public
Government
Sovereign cloud with full audit trails
eIDAS · FIPS 140-2
Fleet & IoT
Logistics
Real-time fleet tracking & IoT ingestion
MQTT · OPC-UA
15+Years building and running production systems, since 2011
24/7Operations and on-call
KubernetesNative training and serving
Open sourceMLflow, Kubeflow, KServe, vLLM
What we deliver
Our MLOps services
From the first training pipeline to GPU-backed inference in production
Replace hand-run notebooks with reproducible pipelines. We orchestrate data preparation, training and evaluation as versioned steps, and add a feature store so training and serving read the same feature definitions.
Kubeflow · Airflow · FeastKnow which model is in production, what data trained it and who approved it. We set up a model registry with lineage, staged promotion and rollback, wired to your CI/CD so a model ships like any other release.
MLflow · lineage · staged promotionServe classic models and LLMs behind stable, autoscaled endpoints on Kubernetes. We deploy KServe, vLLM and Triton with canary rollouts, request batching and scale-to-zero for idle models.
vLLM · KServe · TritonGPUs are the most expensive line in an AI budget. We configure GPU node pools, the NVIDIA GPU Operator, quotas and job queueing, MIG or time-slicing for small workloads and spot capacity for training — with cost visible per team.
GPU Operator · MIG · KueueA model can degrade without a single error in the logs. We monitor latency, throughput and GPU use alongside data drift, prediction drift and quality metrics, and alert before your users notice.
Prometheus · Evidently · GrafanaRun LLM applications with the same discipline as code. We version prompts and model configurations, run evaluation suites on every change, trace requests end to end and track token cost per feature.
Prompt versioning · evals · tracingFree assessment
Get a free MLOps assessment
Our engineers review your current setup and deliver a prioritized roadmap — no strings attached.
Who we help
Teams taking AI to production
The four situations where this engagement usually pays back fastest.
Product Teams Shipping LLM Features
You are putting an LLM into a product and need prompts, evaluations and costs under control. We set up the LLMOps loop so every change is versioned, tested and traceable.
Companies Self-Hosting Open-Weight Models
Data residency, privacy or cost push you to run models on your own infrastructure. We build the GPU platform and the inference layer on Kubernetes, in your cloud or on-premises.
Platform Teams with Unpredictable GPU Bills
GPUs sit idle while teams wait for capacity. We add scheduling, quotas, sharing and cost attribution so every GPU hour is used and accounted for.
Own product
SRExpert AI-powered Kubernetes operations
SRExpert is Privum's own AI-powered SRE platform for Kubernetes. We build and operate it ourselves — AI working inside a production Kubernetes platform, not in a demo.
Tech stack
KubernetesPrometheusOpenTelemetryGrafana
Running many Kubernetes clusters spreads metrics, logs, traces, alerts, policies and costs across separate tools, and engineers lose time correlating them in the middle of an incident.
We built SRExpert: unified observability, incident response automation, SLOs and error budgets, policy guardrails and cost insights across clusters, plus an AI assistant that answers natural-language questions with fleet context and generates kubectl workflows.
One control plane for SRE, security and platform teams across AWS, Azure, GCP and on-premises clusters — and first-hand experience running AI inside a production platform, which we bring to every MLOps engagement.
Deploy, verify, serve
Model serving, declared as code
An inference service is a Kubernetes resource like any other: reviewed in a pull request, rolled out by the pipeline, autoscaled on GPU nodes and observable from day one.
~/modelsready
$kubectl apply -f inference-service.yaml -n models$kubectl get inferenceservice llm-chat -n models$curl -s $ENDPOINT/v1/chat/completions -d @request.json✓inferenceservice/llm-chat READY · runtime: vLLM · nvidia.com/gpu: 1 · HTTP 200
K8sNative inference
GPUScheduling & quotas
0Vendor lock-in
Outcomes & method
MLOps for models that last in production
Getting a model to work once is the easy part; keeping it reproducible, served and measured is where AI projects stall. We build the pipelines, the serving layer and the monitoring that make model releases routine and keep models accountable after launch — on infrastructure your team can run.
Business outcomes
- 01
Repeatable model releases
Every model is trained by a pipeline, registered with its lineage and promoted through the same reviewed path as application code.
- 02
Predictable GPU spend
Scheduling, sharing and scale-to-zero keep GPUs busy when there is work and released when there is not, with cost visible per team.
- 03
Models you can trust in production
Monitoring for drift, quality and latency tells you a model needs retraining before your users notice.
How we implement
- 01
Assess
We review your models, data pipelines, infrastructure and GPU use, and map the path from notebook to production.
- 02
Design the platform
We choose the stack with you — orchestration, registry, serving runtime, GPU strategy — preferring open source and the cloud or on-premises estate you already run.
- 03
Build the pipelines
We implement training and feature pipelines, the model registry and CI/CD for models, starting with one model end to end.
- 04
Serve & observe
We deploy the inference layer on Kubernetes with autoscaling, canary rollouts and monitoring for latency, drift and cost.
- 05
Hand over & operate
We document the platform, train your team and either step back or keep operating it with you, 24/7 if needed.
Engagement model
How we work
From first call to production — a proven 4-step engagement model that keeps the conversation transparent and the velocity honest.
- 01
Discovery
We audit your current stack, identify gaps, and align on business goals.
- 02
Assessment
A detailed roadmap with priorities, effort estimates, and quick wins.
- 03
Delivery
Our engineers embed with your team and execute sprint by sprint.
- 04
Support
Ongoing monitoring, optimization, and knowledge transfer to your team.
Related disciplines
Related services
Adjacent practices that pair well with this one — most engagements blend two or three.
Common questions
Frequently asked questions
Practical answers about scope, timelines, and how engagements with our MLOps team usually look.
Managed platforms are the fastest start when you are committed to one cloud and your workloads fit their conventions. Kubernetes makes sense when you need portability across clouds or on-premises, already run Kubernetes, want to self-host open-weight models, or need fine control over GPU cost. Many teams combine both — managed training with serving on Kubernetes, for example. We assess your workloads and recommend the split, not a single tool.
By making sure GPUs only run when they do useful work. We right-size instances to the model, share GPUs with MIG or time-slicing for small workloads, scale idle inference endpoints to zero, queue training jobs instead of over-provisioning, use spot capacity for interruptible training, and apply quantization and request batching to serve more traffic per GPU. Cost is attributed per team and per model, so you can see where the money goes.
Yes. We build GPU-enabled Kubernetes clusters on bare metal or private cloud, with the NVIDIA GPU Operator, a private model registry and an internal inference layer, so models and data never leave your infrastructure. It is a common setup for open-weight LLMs when data residency or confidentiality rules out public APIs.
We track three layers: service metrics (latency, throughput, errors, GPU use), data and prediction drift against the training baseline, and quality metrics once ground truth or user feedback arrives. Thresholds trigger alerts and, where it makes sense, a retraining pipeline — with a person approving the promotion of the new model.
MLOps covers the lifecycle of models you train: data, features, training, registry, serving and monitoring. LLMOps adds what changes when you build on large language models, often ones you do not train: prompt and configuration versioning, evaluation suites for quality and safety, tracing of multi-step chains and agents, token cost tracking and guardrails. Most teams building with LLMs need both, and we design them as one platform.
Yes, and it is a common starting point. We refactor notebook code into tested, parameterized pipeline steps, version the data and the model, register the result and deploy it behind a monitored endpoint. Your data scientists keep using notebooks to explore; production runs from the pipeline.
Talk to engineering
Let's talk about your MLOps strategy
Whether you're starting from scratch or scaling what you have, our engineers are ready to help.