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The Intersection of Cloud Native and Artificial Intelligence: Challenges, Opportunities, and the Path Ahead

Anjul Sahu

Anjul Sahu

Artificial intelligence (AI) and cloud native technologies are two of the most transformative forces shaping the technology landscape today. As organizations increasingly look to leverage AI to drive innovation and boost competitiveness, the scalable and resilient infrastructure promised by cloud native architectures becomes ever more critical. However, effectively combining these two domains comes with complexities.

In this blog, we will explore the intersection of cloud native and AI, the key challenges, and the opportunities this fusion presents.

Why Cloud Native AI?

The on-demand scalability and reliability of cloud infrastructure has fueled the rapid growth of AI, enabling access to vast compute resources for training complex models. Cloud native builds on this with technologies like containers, microservices, and orchestrators that provide modular, resilient environments to develop and deploy AI at scale. As the Cloud Native Computing Foundation notes, “cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments.”

For AI developers, cloud native means the ability to package models and dependencies into containers and deploy them seamlessly via orchestrators like Kubernetes. This supports portability across environments, from on-premises data centers to public clouds. For data scientists and ML engineers, cloud native tooling like Kubeflow simplifies building ML pipelines and putting models into production. On the infrastructure side, leveraging Kubernetes for dynamic resource management helps optimize expensive AI workloads.

Cloud native architecture provides several benefits when it comes to AI workloads. Here are some of the ways in which cloud native can help:

  1. Scalability: Cloud native architecture allows for easy scaling of resources, making it ideal for AI workloads that require a lot of computing power. With cloud native, you can quickly and easily add or remove resources as needed to meet the demands of your AI workload.
  2. Flexibility: Cloud native architecture provides flexibility in terms of deployment options, allowing you to deploy your AI workloads on-premises or in the cloud. This allows you to choose the best option for your specific needs and budget.
  3. Resilience: Cloud native architecture is designed to be resilient, with built-in redundancy and failover mechanisms that ensure your AI workloads are always available. This is particularly important for mission-critical applications where downtime can have significant consequences.
  4. Cost efficiency: Cloud native architecture can help you save money on IT infrastructure costs by allowing you to use shared resources across multiple workloads. This can lead to cost savings and a more efficient use of resources.
  5. Security: Cloud native architecture provides security features that help protect your AI workloads from threats such as data breaches and cyber attacks. This includes encryption, access controls, and other security measures that are designed to keep your data safe.

In short, cloud native gives AI development teams agility, automation, and orchestration capabilities to accelerate model development, training, and deployment. It provides the substrate for scalable, resilient ML in production.

Key Challenges at the Intersection

However, several gaps remain in fully unleashing the combined potential of cloud native and AI. Here we examine some of the top challenges.

Managing Complexity

The distributed microservices architecture of cloud native environments adds complexity when orchestrating end-to-end AI workflows. Chains of discrete components for data processing, training, model deployment and more require intricate coordination. ML engineers may also lose visibility into how data flows across microservices. This fragmentation strains productivity.

The evolution of open-source distributed computing engines over the past two decades signifies a shift in focus from data processing workloads to AI/ML workloads. The main difference between these workloads lies in their variety, with the ML model lifecycle encompassing various stages that may require different infrastructure. AI/ML workloads are rapidly changing and present challenges such as serving large language models (LLMs) which require multiple high-end GPUs simultaneously and have a memory-bound performance bottleneck.

Cloud Native, based on microservice architecture, can pose challenges for AI/ML due to the complexity of dealing with each stage in the pipeline as a separate microservice. The fragmented user experience and increased cost associated with integrating each stage from different systems highlight the need for a unified ML infrastructure based on a general-purpose distributed computation engine such as Ray or Kubeflow, which can supplement the existing cloud native ecosystem.

Resource Demands

The compute requirements for large-scale AI training and inference place huge demands on infrastructure resources. Supporting the needs of diverse models — from deep learning to LLMs like GPT-3.5 and llama — with optimal hardware utilization remains challenging. There are also complexities around scheduling scarce accelerator resources like GPUs.

Accelerators such as GPUs, and TPUs are becoming popular for training LLMs and supporting inference due to their varied compute resources. However, these accelerators require drivers, configuration, and scheduler enhancements. There is a limited availability and cost of accelerators is high.

Observability & Monitoring

The dynamic nature of AI workloads makes monitoring and troubleshooting distributed cloud native applications difficult. Tracing requests and metrics across complex microservices architectures is hard. This hampers performance management and bottleneck identification.

Model Governance

As organizations scale up AI adoption, they need robust model governance including version control, model monitoring, explainability, and more. Lifecycle management and model reproducibility are still pain points in multi-tenant cloud native environments.

Skills Gap

Many data scientists are not proficient in cloud native technologies. The learning curve to leverage Kubernetes, microservices, CI/CD pipelines discourages adoption. Abstraction layers and managed services are not yet mature. This skills gap slows momentum.

Sustainability

Training ever-larger AI models demands skyrocketing compute resources, raising sustainability concerns. Tracking and optimizing the carbon footprint of cloud native AI workloads remains challenging. methodologies to measure and mitigate environmental impact are still emerging.

The Path Ahead: Opportunities for Cloud Native AI

While formidable, these challenges also open up opportunities for innovation to enable responsible and scalable cloud native AI.

For example:

  • Tools for end-to-end orchestration, like Kubeflow, that simplify deploying and managing multi-stage AI pipelines as microservices.
  • Advances in elastic training frameworks that leverage Kubernetes to dynamically scale resources for AI workloads.
  • Approaches like distributed training that spread workload across compute resources for accelerated, lower-cost model development.
  • Hybrid cloud and edge computing strategies to geo-distribute inference workloads, improving response times and optimizing infrastructure costs.
  • Open source model governance solutions to add transparency, auditability, and reproducibility to AI workflows.
  • Methodologies to benchmark workload carbon emissions and optimize energy efficiency across cloud native environments.
  • Investment in skills development through training programs, documentation, and on-boarding guidance targeted at data scientists.
  • Higher level abstractions, SDKs and managed services to reduce cloud native complexity for AI developers.
  • Leveraging AI itself to enhance cloud native management, improving automation and optimizing resource utilization.

Relevant OSS Projects in this space

Below is the list of some relevant open source projects. You can find more projects at Linux Foundation AI Landscape.

General Orchestration

ML Serving

CI/CD - Delivery

Data Science

Workload Observability

AutoML

Distributed Training

Model/LLM Observability

AI Gateway

Vector Databases

Data Architecture

Governance, Policy & Security

Conclusion

The cloud native ecosystem offers fertile ground to cultivate these innovations via open source communities developing shared tooling for the AI domain. As complementary technologies, uniting the strengths of cloud native infrastructure and the incredible potential of AI will shape the next phase of business innovation and digital transformation. Organizations that lead in responsible and scalable cloud native AI will have a competitive edge. But unlocking the benefits will require persistence and collaboration to tackle the multifaceted challenges along the way.

Read more:

How CloudRaft can help implementing Cloud Native Solutions for AI/ML Workload

At CloudRaft, we specialize in providing cloud native solutions to help AI companies overcome the challenges outlined in this article. Our team of experienced professionals has a deep understanding of both cloud technology and artificial intelligence, allowing us to provide tailored solutions that address the unique needs of our clients.

One of the key benefits of working with CloudRaft is our ability to leverage the latest advancements in cloud native technology to help our clients achieve their goals.

We are committed to providing our clients with the expertise and support they need to succeed in the rapidly growing field of AI. Whether you're looking to build a new application from scratch or optimize an existing one for the cloud, we have the experience and knowledge to help you achieve your goals.

Please contact us to discuss your specific problem statement.

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