A model-as-a-service approach to accelerating AI value

In this blog, we will learn about a model-as-a-service approach to accelerating AI value.

Introduction

Artificial Intelligence (AI) is revolutionizing the way organizations harness data, providing valuable insights that drive innovation. Many enterprises are adopting AI-driven applications, but developing, training, and managing AI workloads at scale presents significant challenges. Model-as-a-Service (MaaS) addresses these issues by offering scalable AI models as a service, allowing organizations to streamline AI adoption and operationalization.

What is Model-as-a-Service (MaaS)?

MaaS is a solution that enables businesses to deploy AI models by providing pre-trained models through an API gateway on a hybrid cloud AI platform. It facilitates quicker time-to-value by eliminating the need for teams to develop and manage models from scratch.

Organizations can either develop and maintain their MaaS platform in-house or rely on trusted third-party providers. In either case, MaaS simplifies AI deployment, allowing teams to focus on strategic initiatives. Additionally, it offers control over data privacy and governance, reducing concerns related to security and compliance.

When is MaaS Beneficial?

Managing AI infrastructure, particularly GPUs, requires skilled professionals. Instead of focusing solely on AI Infrastructure-as-a-Service (IaaS), an enterprise can dedicate a small team to developing and deploying AI models that can be used across the organization.

MaaS providers handle infrastructure scaling, ensuring AI models can meet increasing inference demands. Additionally, they manage system maintenance, updates, and security, reducing operational overhead for businesses.

Cost efficiency is another crucial benefit. GPUs are expensive, and inefficient utilization can drive up expenses. MaaS optimizes infrastructure usage, minimizing upfront investments and operational costs. Furthermore, organizations benefit from faster return on investment (ROI), as AI models are readily accessible for immediate application.

Understanding MaaS Components

To effectively implement MaaS, organizations need a robust architecture that includes AI models, a scalable AI platform, AI orchestration capabilities, and efficient API management.

Models

MaaS providers build a model catalog using open-source, third-party, or proprietary AI models. Depending on specific needs, they may customize these models through fine-tuning or techniques such as retrieval-augmented generation (RAG) or retrieval augmentation with fine-tuning (RAFT). Once optimized, models are stored in a registry and made available through an API for easy access. Developers can explore available models and integrate them seamlessly through a dedicated developer portal.

AI Platform: Red Hat OpenShift AI

The foundation of a MaaS system is a powerful AI platform that supports model tuning, deployment, and monitoring. The MaaS provider ensures the platform has the necessary observability tools to track performance and security.

An effective MaaS system must support multi-tenancy, integrate with various data sources, and provide security measures to protect models and infrastructure. In this context, Red Hat OpenShift AI is a strong choice, offering multi-cloud deployment capabilities, robust security features, and seamless integration with data services.

AI Orchestration

AI orchestration allows MaaS providers to manage multiple versions of a model or deploy different models for a particular use case. The orchestration layer ensures that API requests are routed to the appropriate model instance and may include components for model tuning and optimization.

API Management

API management is a crucial aspect of MaaS, enabling access control, analytics, and monetization. A robust API management system ensures that organizations can monitor usage patterns, prevent misuse, and implement security policies.

Red Hat 3scale API Management is an example of an advanced API management system offering high availability, traffic control, authentication, identity provider integration, and developer workflows.

Enabling Intelligent Applications

MaaS simplifies the development of intelligent applications such as chatbots, mobile apps, and web portals. Developers can integrate AI models into their applications via published APIs, eliminating the complexity of model training and infrastructure management. A developer portal facilitates onboarding and API access, ensuring efficient application development.

Conclusion

MaaS abstracts the complexities of AI infrastructure and MLOps, allowing businesses to accelerate AI adoption while maintaining cost efficiency. As AI continues to gain traction, MaaS offers a scalable approach to deploying intelligent solutions. Red Hat OpenShift AI provides a strong foundation for building an MaaS offering, helping organizations unlock the full potential of their AI investments.