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- Implement version control for models, ensuring outdated versions do not overwrite newer.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.