AI Model Onboarding: A Balancing Act Between Security and Scalability
The responsible deployment of artificial intelligence (AI) models demands rigorous governance and effective onboarding protocols. This ensures that each model is safe, performs as expected, and adheres to all relevant compliance standards. A strategy that integrates the use of virtual machines (VMs) and containerization technologies, orchestrated with Kubernetes, provides an excellent approach. This method delivers the necessary isolation during the testing phase while supporting scalability in production environments.
The Model Onboarding Process
When data scientists identify promising AI models, perhaps from repositories like Hugging Face or NVIDIA GPU Cloud (NGC), the initial step involves a thorough assessment of their security and performance. This means confirming the model operates as intended and does not introduce any vulnerabilities. Tools like Giskard are invaluable in this process.
Giskard acts as a black-box testing platform, allowing detailed evaluations of AI models. It identifies potential issues such as biases, security gaps, and the possibility of generating harmful outputs. Black-box testing is particularly useful because it doesn’t require access to the model’s internal structure, allowing flexibility in the evaluation process.
Ensuring Isolation and Safety During Testing
Testing new AI models necessitates an environment where robust isolation is a priority to guard against security breaches. While Kubernetes clusters offer advanced orchestration and scalability benefits, configuring them for strong isolation is complex. Using VMs for initial testing provides inherent isolation, thereby preventing unverified models from interacting with other active processes. This approach simplifies the security setup, enabling data scientists to focus on comprehensive testing and validation.
Transitioning to Production: Harnessing the Power of Kubernetes
Once a model has successfully passed all assessments, it can be packaged and stored in a local model repository such as Harbor, which supports OCI-compliant container images. This packaging streamlines its integration with Kubernetes clusters. Kubernetes’ cloud-native orchestration and automatic scaling are critical for handling variable workloads in production environments.
The recent introduction of the ImageVolume feature in Kubernetes v1.31 further enhances this process. It allows OCI-compliant images to be used as native volume sources directly within pods. This capability simplifies AI model deployments by enabling the direct mounting of model artifacts, reducing the complexity involved in managing model files separately.
Continuous Deployment and Resource Management
Deploying AI models is not a one-time procedure, but an ongoing process of monitoring and improvement. In production, models typically undergo a lifecycle of six to ten months before being replaced or updated. During this period, data scientists often conduct A/B testing to compare the performance of existing models with new iterations. Kubernetes aids this iterative process through features like gradual rollouts, which enable the incremental introduction of new models. This approach minimizes operational risks and ensures the maintenance of service level objectives (SLOs). For instance, traffic can be directed partially to the new model, gradually increasing as confidence in its performance grows. Tools like Istio can also provide assistance in managing traffic routing within Kubernetes environments.
Efficient resource management is also essential, especially given the limited supply and high demand for GPU resources. While Kubernetes facilitates dynamic resource allocation, fragmentation can occur as instances are created and terminated in various patterns. Implementing robust resource management strategies optimizes hardware resource utilization, thereby maintaining performance and cost-effectiveness.
Conclusion
Effective AI model governance and onboarding require a well-integrated balance of security and scalability. By leveraging VMs for isolated testing and Kubernetes for scalable deployment, organizations can create a robust framework that supports the continuous integration and delivery of AI models. Incorporating tools like Giskard and embracing advancements like the Kubernetes ImageVolume feature can further enhance this framework, ensuring that AI models are dependable and efficiently managed throughout their entire lifecycle.
