AI Execution Layer - OpenShift AI

The Modern AI Challenge: Bridging Innovation and Enterprise Execution

‍

Tools Involved: RedHat OpenShift AI, NVIDIA, Dell AI Factory

Delivered by: MOBIA Platform Engineering

‍

‍

The Business Challenge: Stalled AI Initiatives

Enterprises are investing heavily in AI experimentation, but without a unified execution layer, initiatives often stall before reaching production. Fragmented toolchains, inconsistent infrastructure, and governance limitations restrict scalability and operational efficiency.

Accelerate Value Delivery:

Move AI workloads from experimentation to production faster through a unified OpenShift AI environment.

Leverage Any Infrastructure:

Enable AI across on-prem, edge, and cloud using certified integrations with Dell AI factory and NVIDIA technologies.

Optimize Cost and Performance:

Run interference and training where it makes the most sense - across GPU or Dell AI Factory-based compute environments.

Ensure Governance and Compliance:

Centralize workload management and governance with Ansible Automation and policy-drive automation.

‍

The Solution: Two-Pillar Path to Operationalized AI

Our structured approach establishing a robust AI foundation and scales operational excellence across all environments

‍

PILLAR 1: AI PLATFORM FOUNDATION (BUILD AND ENABLE)

This pillar focusses on establishing the core, flexible platform for all future AI work.

‍

What We Do

Deploy OpenShift AI and integrate it with your existing compute, storage, and network infrastructure using NVIDIA and Dell AI Factory solutions.

Business Value

Unified AI Platform: Replace fragmented experimentation with a scalable, secure AI middleware layer.

Alignment

Establish a standardized, governed, and secure AI platform foundation for your organization.

‍

PILLAR 2: AI PERATIONS AND INFERENCE (RUN AND OPTIMIZE)

This pillar focusses on operationalizing models for high availability and continuous delivery of insights.

‍

What We Do

Configure OpenShift Inference Server for model deployment and lifecycle management, intergrating automation and Ansible.

Business Value

Faster Time-to-Production: Standardize data science workflows using Jupyter Notebooks and integrated MLOps pipelines.

Alignment

Operationalize AI workloads across hybrid environments with full visibility, compliance, and automation.

‍

‍