Run AI at scale.On infrastructure you own.
AI doesn't have to mean surrendering your data to a hyperscaler. Red Hat's open AI platform lets you build, train, fine-tune, and serve AI/ML models on hybrid cloud — with the governance, security, and portability your enterprise demands.
Most AI strategies stall at one of three places: GPU infrastructure that nobody knows how to size, foundation-model fine-tuning where the data can't leave the building, or governance frameworks that aren't ready for a regulator. OpenShift AI solves all three on infrastructure you own — and Infizia delivers it from readiness assessment to MLOps to responsible AI.
01 · Data inlet
On-prem corpus
MinIO · Ceph · 14M docs
02 · Training crucible
InstructLab fine-tune
Granite-13B · 3 epochs · 4 GPU
03 · Inference outlet
Production endpoint
KServe · /v1/models/granite
GPU rack · NVIDIA operator
4 × A100
AI ambition without AI infrastructure is a slide deck.
Every enterprise board has signed off on AI. Most enterprise data-science teams are still waiting for GPU access, a model-serving platform that meets compliance, and a governance framework that survives an audit.
Friction
Data residency vs. hyperscaler AI
Regulated data can't go to a public LLM endpoint. The compliance team blocks the integration. The data-science team can't ship the use case. Both sides escalate.
Friction
GPU infrastructure that nobody sized
Procurement bought GPUs without knowing the workload. Three months later, half the cards are idle and the model that needs them is starved. Nobody owns the math.
Friction
Models that can't get to production
Notebook to production is a six-month journey nobody signed up for. Models live on data scientists' laptops because there's no serving platform, no MLOps, and no GitOps for ML.
Friction
Governance that fails the audit
Bias evaluations done ad-hoc. Model cards written by hand. Inference logs not retained. The first regulator question takes the team weeks to answer — and the answer is incomplete.
Five services that take OpenShift AI from board mandate to production.
Readiness assessment, full platform deployment with NVIDIA GPU operators, LLM fine-tuning via InstructLab on your data, MLOps pipelines, and a responsible AI framework that meets regulatory bar.
Service · 01
AI Readiness Assessment
Evaluate readiness to adopt enterprise AI — infrastructure (GPU, storage, networking), data maturity, team skills — and define a phased AI adoption roadmap.
- AI infrastructure gap analysis
- Data readiness assessment
- GPU workload sizing and ROI model
- AI use case prioritisation matrix
- OpenShift AI architecture blueprint
Service · 02
OpenShift AI Platform Deployment
Production-ready OpenShift AI environment — GPU node provisioning, NVIDIA operator setup, model serving infrastructure, and data science user environment.
- OpenShift cluster with GPU worker nodes (NVIDIA / AMD)
- NVIDIA GPU Operator and CUDA toolkit installation
- OpenShift AI operator deployment and configuration
- MinIO / Ceph / S3 object storage integration
- User workbench environment provisioning
- Model serving infrastructure (KServe / ModelMesh)
- Monitoring (model metrics → Prometheus → Grafana)
Service · 03
LLM Fine-Tuning & Deployment
Fine-tune open-source foundation models on proprietary data using InstructLab — and deploy them as production inference endpoints, keeping data entirely within your infrastructure.
- IBM Granite (7B · 13B · 20B · 34B variants)
- Meta Llama 3 / 3.1
- Mistral / Mixtral
- Code Llama / StarCoder (developer tooling)
- Use cases: knowledge-base Q&A · document summarisation · code review · customer support · regulatory analysis
Service · 04
MLOps Pipeline Build
End-to-end MLOps so model development, training, evaluation, and deployment follow a repeatable, version-controlled, and auditable process.
- Data ingestion and validation
- Feature engineering pipelines
- Model training with experiment tracking (MLflow)
- Model evaluation and approval gates
- Model packaging (containerisation)
- Staging and production deployment via GitOps
- Model performance monitoring and alerting
Service · 05
AI Governance & Responsible AI
A responsible AI framework on your OpenShift AI platform — explainability, bias detection, audit logs, and access controls. Built for regulated industries.
- Model cards and documentation standards
- Bias and fairness evaluation (AI Fairness 360 / Fairlearn)
- Explainability integration (SHAP · LIME)
- Inference audit logging
- RBAC for model access
- Data lineage tracking
Open-source models, on your data , in your cluster.
InstructLab + OpenShift AI lets you fine-tune leading open-source foundation models on proprietary data — without that data ever leaving your infrastructure.
Granite 7B / 13B / 20B / 34B
IBM
Llama 3 / 3.1
Meta
Mistral / Mixtral
Mistral AI
Code Llama / StarCoder
Developer tooling
What teams ship on OpenShift AI today.
Six industry archetypes from real engagements — each one running on a different combination of OpenShift AI components: KServe, Granite, InstructLab, Kubeflow, MLflow, Pipelines.
Industry
Banking & BFSI
Fraud detection model serving at scale
Industry
Healthcare
Medical document summarisation (on-prem LLM)
Industry
Government
Regulatory compliance Q&A chatbot
Industry
Manufacturing
Predictive maintenance with sensor data
Industry
Telecom
Network anomaly detection
Industry
Retail
Demand forecasting and inventory optimisation
Four entry points into OpenShift AI.
Workshop your readiness, fixed-scope deployment, fine-tune your first foundation model, or upskill the data-science team — pick the entry point that matches the stage of your AI programme.
Model 01
AI Readiness Workshop
Infrastructure gap analysis, data readiness, GPU sizing model, use case prioritisation, and architecture blueprint.
Model 02
Platform Deployment
Production-grade OpenShift AI cluster with GPU nodes, NVIDIA operator, model serving, and the first MLOps pipeline.
Model 03
AI Platform Tier
Managed OpenShift AI operations — cluster, model serving, MLOps pipeline, governance, and ongoing fine-tuning support.
Model 04
OpenShift AI Training
Red Hat AI / OpenShift AI Fundamentals — virtual delivery, full lab environment, group discounts for 5+.
Sovereign AI. Auditable. Production-grade.
Models that fine-tune on your data without leaving your network. MLOps that promotes from notebook to production through GitOps. Governance that survives a regulator visit.
Outcome
Data never leaves
Foundation models fine-tune in your cluster on your data. Inference endpoints sit behind your network policy. Compliance teams sign off.
Outcome
Notebook → prod in days
MLOps pipeline with MLflow + KServe + GitOps — model promotion follows the same release cadence as application code.
Outcome
GPUs at 70%+ utilisation
Right-sized NVIDIA GPU operator deployment + ModelMesh — no idle cards, no starved workloads, sized to the actual model fleet.
Outcome
Audit-ready by default
Model cards, bias reports, explainability, inference logs, and data lineage — generated as part of every deployment, not after the regulator asks.
Let's scope this
for your stack.
Walk through a tailored openshift ai platform engagement with our team — capability fit, sequencing, timeline, and pricing scoped for your context. Or grab the corporate brochure for the full Infizia overview at your own pace.