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Artificial Intelligence Center Of Excellence

At Smart Scale Engineering, our Artificial Intelligence (AI) Center of Excellence is a strategic hub where centralized team whose primary role is to nurture the strategic hub for all Artificial Intelligence initiatives inside the company or towards our customers.

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Our AI Values

Whether you’re starting with a proof of concept or scaling enterprise-wide AI, our CoE guides you through every step with a clear focus on technical precision and ethical alignment.

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Trust

We strive for technical excellence and operational rigor in every project. From architecture to deployment, our teams deliver solutions that perform, scale, and endure.

Innovation with integrity

We believe in innovation that respects people, the planet, and ethical principles. Our teams explore new technologies with curiosity and responsibility, ensuring they serve real-world needs.

Client-centered collaboration

Your challenges are our roadmap. We work closely with you to co-design tailored solutions, ensuring clarity, alignment, and impact from day one.

Sustainability by design

Environmental performance is built into our engineering process. We develop architectures and operations that reduce carbon footprints and promote digital sobriety.

Key Objectives

Faster AI Adoption: we streamlines processes, provides reusable models and tools, allowing AI initiatives to move from pilot to offerings to our customers more quickly.

High-Impact Prioritization: The CoE evaluates and prioritizes AI use cases based on their potential to drive measurable business value (e.g., revenue growth, cost reduction), ensuring investment is focused on the most impactful projects to bring value to our customers.

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Key Objectives

Centralized Expertise: we consolidate top AI talent, creating a shared pool of knowledge, skills, and resources available to the entire organization.

Standardized Best Practices: The AI CoE at Smart Scale Engineering establishes a uniform set of development methodologies, toolsets, and deployment pipelines (MLOps), ensuring consistency and quality across all AI projects.

Upskilling and Collaboration: It acts as a hub for training, workshops, and cross-functional collaboration, raising the general AI literacy and capability of the entire workforce.

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AI CoE Function

Smart Scale AI CoE is the central authority that guides and enables our strategy and offerings maturity in DevOps.

AI Governance, Ethics, and Risk Management

Establish and enforce policies for the responsible use of AI.

Knowledge Sharing and Talent Development

Serve as the centralized hub for AI expertise and best practices.

Advanced Research and Innovation

Explore, experiment with, and integrate cutting-edge AI technologies (like Generative AI) for future business application.

Use Case Ideation and Prioritization

Generate and evaluate potential AI project opportunities across all business units.

AI CoE Role

Smart Scale AI CoE is the central authority that guides and enables our strategy and offerings maturity in DevOps.

AI Governance, Ethics, and Risk Management

Oversee ethical standards, manage regulatory compliance (data privacy, security), detect and mitigate risks like algorithmic bias, and ensure transparency in models.

Knowledge Sharing and Talent Development

Conduct organization-wide training, provide mentorship, develop AI literacy programs, and maintain a repository of shared documentation, successful project templates, and lessons learned.

Advanced Research and Innovation

Run pilot projects and proofs-of-concept (PoCs), and manage relationships with external partners, vendors, and academic institutions to stay ahead of the technology curve.

Use Case Ideation and Prioritization

Create a formal intake process and a scoring rubric (based on business impact, feasibility, and risk) to create a constantly prioritized backlog of projects.

The Benefits of Industrializing AI with Red Hat OpenShift AI

In organizations across the globe, a familiar story plays out every day. A team of brilliant data scientists, working in their own specialized environment, develops a groundbreaking machine learning model. But when the time comes to deploy that model into a production application, the process grinds to a halt. The model, painstakingly built in an artisanal « workshop, » struggles to survive on the enterprise « factory floor. »

 

This is the « lab to production » gap, a chasm of friction between data science teams who need flexibility and powerful tools, and IT operations teams who demand security, scalability, and governance.

Red Hat OpenShift AI is a unified platform designed to bridge this exact gap. It’s not just another collection of AI/ML tools; it’s a comprehensive, enterprise-ready platform that brings the entire AI/ML lifecycle onto the trusted, scalable foundation of Kubernetes. It provides the common ground where data science and enterprise IT can meet to industrialize AI and deliver business value at speed.

What is OpenShift AI? A Unified Platform

OpenShift AI integrates a curated set of leading open-source AI/ML tools and technologies into a single, cohesive platform running on Red Hat OpenShift. This provides a consistent environment for every stage of the lifecycle, including:

  • Data preparation and analysis with tools like Starburst and Trino.
  • Model development and experimentation in familiar Jupyter notebooks.
  • Model training with frameworks like TensorFlow and PyTorch, with easy access to GPU acceleration.
  • Model serving and monitoring using solutions like KServe and ModelMesh.
  • MLOps pipelines to automate the entire workflow using tools like Kubeflow.

By providing these capabilities on a single platform, OpenShift AI delivers powerful benefits to every stakeholder involved.

The Key Benefits: A Platform Approach to AI

For Data Scientists: A Sandbox with Superpowers

Data scientists are freed from the constraints of their local machines. OpenShift AI gives them the self-service, familiar tools they love, but with the power of the enterprise behind them.

  • On-Demand Resources: They can easily request and access the powerful computing resources they need, such as GPUs, for training complex models without waiting for IT tickets.
  • Centralized Data Access: The platform simplifies secure access to large, distributed datasets from across the enterprise.
  • Collaboration: It provides a shared, consistent environment, ending the « it works on my machine » problem and making it easier for data science teams to collaborate on projects.

For ML Engineers: A Streamlined Path to Production (MLOps)

Getting a model into production and keeping it there is the core challenge of MLOps. OpenShift AI provides the essential plumbing to automate this process.

  • Repeatable Pipelines: It enables the creation of robust, automated MLOps pipelines to handle everything from data ingestion and model training to validation, deployment, and monitoring.
  • Faster, Safer Deployments: By bringing the discipline of CI/CD to machine learning, it allows for faster, more frequent, and more reliable model deployments.

For IT Administrators: Consistency, Governance, and Security

This is where the platform approach shines. For IT and platform teams, OpenShift AI brings sanity to the often-chaotic world of AI infrastructure.

  • A Single Platform to Manage: Because AI workloads run as containers on OpenShift, they can be managed, monitored, and secured using the exact same tools and practices as all other standard applications.
  • Centralized Governance: IT can enforce consistent security policies, role-based access control (RBAC), and resource quotas for all AI/ML projects, eliminating « shadow IT » and reducing risk.

For the Business: Faster Time-to-Value and Reduced Risk

Ultimately, the technical benefits translate into tangible business outcomes.

  • Accelerated Innovation: By streamlining the entire AI/ML lifecycle and removing friction between teams, OpenShift AI dramatically reduces the time it takes to get a model from an idea to a value-creating production application.
  • Improved ROI: It ensures that the significant investment made in data science talent and AI development actually translates into deployed solutions that impact the bottom line.

Conclusion

Red Hat OpenShift AI provides the essential platform to elevate artificial intelligence from a siloed, experimental craft into a scalable, governed, and industrial-grade enterprise capability. It provides the standardized factory floor needed to consistently build, deploy, and manage AI models with the speed and reliability that business demands.

In an era where AI is a primary driver of competitive advantage, simply having talented data scientists isn’t enough. You must have a robust platform to empower them. OpenShift AI provides that common ground, finally uniting data science and enterprise IT to deliver innovation at scale.

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