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AI Platform
AI
Platform
Many organizations face challenges related to cost, complexity, optimization, security, and scalability when building and operating AI platforms based on Kubernetes and MLOps. As a result, even after successfully completing a PoC or deployment, failures often occur during the operational phase. Daewon CTS offers a range of solutions and services to address these issues and support the successful operation of AI projects.
Infrastructure and Platform
One by one
MLOps/LLMOps, encompassing everything from AI model development to operation, is no longer an option but a necessity. Kubernetes is becoming the foundation of AI operations platforms, and more and more organizations are adopting tools and processes to efficiently utilize it. While this has become a hot topic, this task is not as easy as it sounds. The reality is that there are numerous challenges to be addressed in terms of infrastructure costs, complexity, optimization, security, and scalability.
Challenge
Storage challenges
in AI platform operations
Complex environment
configuration and maintenance
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Kubernetes-based AI platform environments involve complex installation and configuration, requiring extensive experience and expertise for optimization and operations.
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Installing and upgrading tools like Kubeflow is challenging, and documentation or support tailored to enterprise needs is often insufficient.
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Enterprise platforms such as OpenShift also demand significant time and effort to internalize operational capabilities.
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There is a shortage of skilled personnel, such as MLOps engineers.
Security and data
governance issues.
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Model governance and data governance are critical, but applying them to AI platform operations in accordance with an enterprise’s existing security policies and guidelines is challenging.
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Comprehensive security measures—such as IAM integration, network security, encryption, and audit logging—are required in Kubernetes environments, yet there is a shortage of personnel with experience implementing these.
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There is also a lack of experienced personnel to manage output control for LLMs and MMLMs, as well as prompt security.
Performance optimization
tailored for AI workloads
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AI model training and inference workloads require high-performance computing resources.
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Kubernetes’ basic features have limitations in fully utilizing GPUs or optimizing distributed training.
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GPU resource scheduling and virtualization are challenging tasks.
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Optimizing pipelines for different AI workloads is not straightforward.
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Storage I/O bottlenecks can occur when processing large-scale datasets.
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Minimizing latency for real-time inference is also difficult.
On-premises and hybrid cloud scalability
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Maintaining consistent operations is challenging in hybrid environments that use both on-premises and cloud infrastructure.
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Complete consistency is difficult due to factors such as network latency between on-premises and cloud, data synchronization, and differences in Kubernetes versions.
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Integrated management and automated scaling of Kubernetes clusters in a hybrid cloud environment is challenging.
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Given the characteristics of AI workloads, optimizing costs for scale-up and scale-out remains a difficult task.
Service
Optimization services
DIA NEXUS focuses on
Customized AI infrastructure solutions for enterprises
Provides guidance by closely integrating platforms such as Kubernetes (K8s), MLOps, and LLMOps with AI reference architectures tailored to the characteristics of various industries.
Kubernetes-based AI operations optimization
Proposing solutions and approaches to enhance performance and simplify operations based on expertise in Kubernetes- and GPU/NPU–based AI infrastructure resources.
Providing guidance for advanced development
Assessing an enterprise’s AI maturity, establishing MLOps and LLMOps environments suitable for the current stage, and providing guidance for gradual advancement toward more sophisticated AI operations