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Container Service for Kubernetes: AI job overview

Last Updated:Feb 27, 2025

With the cloud-native AI suite of Container Service for Kubernetes (ACK), you can easily and efficiently run AI jobs in your ACK clusters. First, with foundational capabilities such as the Arena CLI and AI workload scheduling, you can perform model training, testing, and performance analysis. Then, through elastic dataset acceleration and heterogeneous GPU resource management, you can deploy model inference services. This topic describes the AI jobs that are supported by the cloud-native AI suite and provides links to the relevant references.

The following table describes the AI jobs that are supported by the cloud-native AI suite.

AI job

Description

References

Model training

You can use Arena to submit various types of training jobs, such as standalone training, distributed training, and elastic training jobs.

Model management

You can manage and associate training jobs and the models generated by training jobs.

Manage models in MLflow Model Registry

Model analysis and optimization

Before deploying a model as a service, you can use Arena to submit model performance analysis and optimization jobs to ensure the model meets the business requirements.

Analyze and optimize models