AI Model Service Platform

Comprehensive AI Model Hosting and Management

A state-of-the-art platform for hosting, managing, and deploying AI models with ease and efficiency. This solution provides seamless integration of model development pipelines, robust version control, and scalable deployment options. Leveraging advanced resource optimization and AI-driven analytics, the platform ensures optimal performance, reduced latency, and streamlined model lifecycle management. Whether for training or inference, this platform empowers businesses to unlock the full potential of their AI capabilities in diverse application scenarios.

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Advantages

Comprehensive Cloud-Native Support

Seamlessly integrates with Kubeflow, Model Registry, and KServe to enhance the cloud-native ecosystem. Simplifies the management of GPU clusters and nodes, significantly improving operational efficiency while allowing technical teams to focus on AI-driven business innovation. The combination of Model Registry and KServe improves model governance and enhances collaboration between Data Scientists, MLOps Engineers, and Business Analysts.

Flexible Computing Power Scheduling

Offers advanced scheduling strategies and resource optimization solutions, leveraging Kubeflow Pipelines to match task requirements accurately. This accelerates model training and improves overall computing performance for both training and inference workloads. KServe’s auto-scaling and GPU scaling ensure efficient use of computational resources for production workloads.

Intuitive Cluster Monitoring

Provides real-time monitoring of cluster resource usage, enabling users to achieve optimal resource allocation and balance. With integration into KServe and Model Registry, users can analyze detailed metrics for stable and efficient cluster operations. This ensures proper tracking of model performance, mitigating issues such as model drift and ensuring smooth operation in production environments.

Powerful Model and Computing Power Management

Supports unified management of heterogeneous computing resources, including GPUs, through Kubeflow and KServe integration. Enables scalable management across thousands of devices, fostering multi-department and multi-task collaboration to enhance productivity and resource utilization. The integration of Model Registry enhances version control, experiment management, and model deployment processes, improving both model efficiency and operational workflows.

Improved Collaboration and Experimentation

The Model Registry fosters collaboration across teams by centralizing model versioning, experiment tracking, and metadata management. Data Scientists can efficiently share models, track performance metrics, and optimize experiments, ensuring smooth transitions to production environments. This results in more informed decision-making and quicker time-to-market for AI solutions.

Enhanced Governance and Reproducibility

Model Registry ensures better model governance and compliance by providing comprehensive model lineage, auditing capabilities, and version control. It enables organizations to comply with regulations such as GDPR and AI-specific acts, making it an essential tool for industries like finance and healthcare. Reproducibility features also allow for the recreation of model experiments, improving transparency and trust in AI-driven decisions.

Capabilities

Comprehensive cloud-native management

The platform enables unified monitoring and management of heterogeneous computing power, encompassing GPU clusters, nodes, naming conventions, user management, and other key components. It offers detailed, traceable functionalities for efficient oversight, ensuring accurate resource utilization. Additionally, it provides comprehensive operation logs and audit trails to support secure and transparent management practices, ensuring that all actions are recorded and easily retrievable for security and accountability purposes.

Flexible scheduling strategy

The platform supports multiple scheduling strategies, including Kubernetes (K8s), Volcano, and custom-defined strategies. It automatically matches computing resources to task requirements, ensuring fast execution with minimal delays. Once a task is completed, the platform autonomously releases the resources, optimizing resource utilization and preventing unnecessary consumption. This dynamic allocation and deallocation of resources help maintain an efficient and responsive computing environment.

Distributed Scheduling

The platform features a powerful scheduling engine capable of managing thousands, or even tens of thousands, of card-level computing resources. It supports multiple modes to address complex computational needs, offering flexibility in scheduling and resource allocation. A personalized placement group strategy is integrated to enhance computing efficiency, ensuring optimal task placement based on workload requirements. This strategy significantly reduces task completion times by minimizing resource contention and improving data locality, further accelerating overall performance.

Convenient task submission

The platform includes a user-friendly visual interface that enables one-click submission of distributed tasks, simplifying the process for users. It comes with built-in support for common computing frameworks, ensuring seamless integration with popular tools. Additionally, the platform offers a mirroring acceleration function, which reduces distribution time by efficiently replicating necessary resources. This feature enhances overall efficiency by minimizing delays in task deployment and enabling faster execution, optimizing the use of available computing power.

Powerful computing power split

The platform supports multi-instance operation of graphics cards, pass-through technology, and multi-node parallel computing to maximize GPU utilization efficiency. By enabling these features, it allows for the concurrent use of multiple GPU instances on a single card, improving resource allocation and task parallelization. Additionally, the platform supports flexible allocation and memory segmentation across multi-brand GPU cards with customized specifications, ensuring that diverse hardware configurations are efficiently utilized and optimized for specific workload requirements, enhancing overall performance and scalability.

Computing power pool management

The platform allows the creation of shared or exclusive computing power pools to meet the resource sharing needs of teams or specific project requirements. This enables efficient allocation of computing resources based on the needs of different users or projects. Additionally, a single GPU card can be shared among different tenants through time-sharing usage, allowing multiple users to access the same hardware at different times. This approach optimizes resource utilization, ensuring that GPU resources are maximally utilized without unnecessary downtime, while maintaining isolation and security between tenants.