Unleashing innovation via Composable Cloud: Customizing AI tech stacks for inference at the edge

Edge operations dominate the CTO agenda today, and as artificial intelligence (AI) and machine learning grow increasingly important across business operations, the CIO, CAIO, CDO, and other leaders are joining the conversation, leaning into edge operations to deliver AI at scale. However, the traditional edge operations designed to support distributed web apps can’t handle the unique needs of distributed AI and machine learning. Instead, enterprises need a tech stack specifically designed to support distributed machine learning operations (MLOps) at scale.

As enterprise leaders contemplate restructuring their edge operations to accommodate the unique needs of serving AI inference at the edge, many are concerned that the rapid evolution of AI and machine learning technology will make new investments obsolete before the enterprise can realize ROI.

The solution for modernizing the tech stack for AI inference at the edge – while also building flexibility to accommodate the constant change in the AI and machine learning technology ecosystem – is composability. 

Composability enables enterprises to select preferred providers at each layer of the cloud stack, trust that all components will work together seamlessly, and switch to new providers and/or new components as their needs change. A composable approach to the tech stack allows enterprises to devise edge operations explicitly built for the demands of AI inference at scale. This approach also future-proofs the organization against the constant evolution of the AI and machine learning ecosystem.

Why inference at the edge?  

As enterprises integrate AI into more business operations, enabling MLOps in edge environments becomes increasingly essential for several reasons: 

• The volume of data processed by large language models (LLMs) and other machine learning models makes it infeasible and prohibitively costly to backhaul data over vast geographical distances. 
• Data governance requires that data remain in the region where it’s collected. 
• The number of AI and machine learning use cases requiring ultra-low latency is growing continuously, making inference at the edge a cornerstone requirement for applications such as GenAI chatbots, robotics, autonomous vehicles, facial recognition, industrial IoT, healthcare monitoring, security, and more.
• Models must be fine-tuned locally using region-specific data to reflect geographical and cultural variations. 

Moreover, just as customer-facing web apps are deployed in edge environments, so are customer-facing AI applications, which need to be distributed across edge locations to support a superior user experience.

Unique requirements for running inference at the edge

The optimal machine learning workflow calls for centrally developing and training models, followed by wide-scale distribution to edge-based cloud environments built for inference, where models are tuned to local data and AI applications are exposed to end users.

Enterprises need access to purpose-built AI and machine learning stacks that include:

• An infrastructure layer that tightly integrates GPUs and CPUs to optimize MLOps, intelligently assigning workloads to the compute resources for efficient, cost-effective processing 
• A platform layer that makes a broad range of development tools and frameworks available to machine learning engineers so they can customize an integrated development environment (IDE) that reflects their preferred way of working
• An application layer that aggregates a wide range of supporting applications that machine learning engineers and operators need to optimize AI application development and deployment 
• Public and private container registries that simplify the development, deployment, and scaling of machine learning and large language models  

Enterprises also need an ecosystem of service providers with specific AI and MLOps expertise to backfill talent gaps in their organization. 

Composability: Ensuring the AI and machine learning cloud stack puzzle pieces fit

There is no single provider of all components needed at all levels of the AI and machine learning stack. Instead, enterprises need to assemble best-of-breed solutions to comprise a complete stack. Neither is there a one-size-fits-all collection of components that is ideal for every enterprise. 

The optimal AI and machine learning stack is unique to every organization and will change along with their needs. As such, interoperability of components at all levels of the stack is essential. Choosing vendors that adhere to the principles of composability allows the enterprise to assemble an AI and machine learning stack that addresses its distinct MLOps needs.

There’s a growing ecosystem of vendors that ascribe to the foundational tenets of composability and offer:

• Complete interoperability: Tech buyers can rest assured that other applications connect easily through APIs.
• Infinite scalability: Cloud-native architecture ensures seamless rolling upgrades that don’t require human intervention.
• Ultimate extensibility: Companies can easily add and replace components and make changes on the fly without disrupting the front-end user experience.
• Flexibility and transparency: Customers avoid vendor lock-in, allowing them to stay in charge of their technology and respond to changing requirements.

Today, there’s no need to be force-fed a pre-packaged AI and machine learning stack that includes components the organization doesn’t need or can’t use. Composability gives organizations ultimate control over the technologies they deploy.

What happens when technology evolves?

We know that change is constant. With the rapid pace of AI innovation, new components at the infrastructure, platform, and application layers will inevitably continue to disrupt MLOps, enabling better inferences.

One of the most advantageous aspects of embracing composability is the future-proofing that the composable approach offers. Because components are fully interoperable and there’s no vendor lock-in, enterprises can replace components at will, either as the organization’s needs change or as new technology becomes available.

The future will be composable AI

We expect composability to emerge as the de facto paradigm shaping AI operations in the coming months. With no concerns over being locked into components or vendor relationships that may quickly outgrow their usefulness, enterprises will be free to focus on maximizing the benefits that AI innovation makes possible.

With the confidence of a composable approach to their AI and machine learning stack, organizations can place big bets on AI innovation by experimenting with meaningful business problems and finding the right mix of components to optimize their machine learning operations.