Potential GSoC Project Ideas for AI/ML in Disconnected Environments with KubeStellar
1. Intelligent Model Deployment & Synchronization in Air-Gapped Clusters
Goal: Enable efficient deployment and synchronization of AI/ML models across disconnected or air-gapped Kubernetes clusters using KubeStellar.
Features:
Automate model distribution from a central hub to edge clusters when connectivity is available.
Implement version control for models, ensuring outdated versions do not overwrite newer.
Introduce a caching mechanism for model artifacts and inference pipelines for offline operation.
Challenges: Handling large model sizes, ensuring integrity and consistency across clusters.
2. AI/ML Pipeline Orchestration for Edge Devices
Goal: Build a lightweight AI/ML pipeline manager for disconnected environments using KubeStellar’s workload synchronization capabilities.
Features:
Define, deploy, and update ML pipelines using a declarative approach.
Implement scheduling policies for AI jobs that adjust based on resource constraints (e.g., CPU, memory).
Introduce offline-first strategies where models can be trained or fine-tuned locally and synchronized when reconnected.
Challenges: Efficient resource allocation on constrained devices, handling intermittent connectivity.
3. Federated Learning Support with KubeStellar
Goal: Enable federated learning (training ML models across multiple disconnected clusters without sharing raw data) using KubeStellar’s workload propagation.
Features:
Implement mechanisms for sharing model updates (gradients, weights) between clusters.
Ensure secure aggregation of models when connectivity is restored.
Optimize update frequency based on available bandwidth and compute power.
Challenges: Privacy and security of model updates, ensuring consistency across federated learning nodes.
4. AI/ML Model Monitoring and Drift Detection in Disconnected Clusters
Goal: Build a monitoring system that detects model drift in disconnected environments and triggers alerts or automatic retraining using KubeStellar.
Features:
Deploy AI models with embedded monitoring hooks that capture drift signals (e.g., statistical changes in input distributions).
Store and sync monitoring metrics when connectivity is restored.
Provide a mechanism for automatic model retraining and redeployment.
Challenges: Efficiently storing and analyzing monitoring data locally, reducing unnecessary sync traffic.
5. Optimized Model Compression and Deployment for Edge Devices
Goal: Integrate automatic model compression (quantization, pruning, distillation) into KubeStellar to optimize AI deployments in disconnected clusters.
Features:
Implement policies that choose between full, quantized, or pruned models based on available resources.
Automate model format conversion for optimized inference (e.g., TensorFlow Lite,).
Sync compressed versions when bandwidth is limited.