System Updates Summary

Recent Platform Enhancements

The KAI platform has been enhanced with several major new features that require deployment updates:

1. Notification & Webhook System

Multi-channel messaging framework supporting in-app, email, SMS, and webhook notifications.

2. Parameter Registry System

Hyperparameter management system for material analysis that stores, retrieves, and suggests optimal parameters based on material type.

3. MultiModal Pattern Recognition

Advanced ML system bridging visual patterns and textual specifications using transformer architecture with cross-modal attention.

4. Domain-Specific Neural Networks

Specialized neural architectures optimized for material texture analysis with custom convolutional filters and attention mechanisms.

Infrastructure Impact

These enhancements require significant infrastructure updates:

• GPU Resources: Additional NVIDIA L40S/H100 GPUs with 48-80GB VRAM for ML workloads
• Kubernetes Components: New deployments, services, and workflow templates
• Storage: Expanded persistent storage for parameter history and ML artifacts
• External Services: Integration with email/SMS providers for notifications

Deployment Checklist

1. Prerequisites

   • NVIDIA GPU operators installed
   • Nodes with appropriate GPUs available
   • Persistent storage configured
   • External service credentials stored as secrets

2. Database Setup

   • Apply notification system migrations
   • Apply parameter registry migrations
   • Configure backup strategy

3. Core Services

   • Deploy notification system
   • Deploy webhook service
   • Deploy parameter registry
   • Update GPU requirements

4. ML Components

   • Deploy multimodal pattern recognition service
   • Deploy domain-specific networks service
   • Configure workflow templates

5. Integration

   • Update existing workflows to use new components
   • Configure monitoring for new services
   • Validate end-to-end functionality

Verification Steps

After deployment, verify:

1. Notification System

   • Send test notifications
   • Verify delivery across channels
   • Test webhook configurations

2. Parameter Registry

   • Query parameters for test materials
   • Test integration with ML pipelines

3. ML Features

   • Submit pattern recognition jobs
   • Verify domain-specific analysis results
   • Check integration with existing visualization

Resource Requirements

Component	CPU	Memory	GPU	Storage
Notification System	2-4 cores	4-8 GB	N/A	10 GB
Webhook Service	2-4 cores	4-8 GB	N/A	20 GB
Parameter Registry	4-8 cores	8-16 GB	N/A	50 GB
MultiModal Pattern Recognition	8-16 cores	16-32 GB	1-2 NVIDIA L40S/H100	100 GB
Domain-Specific Networks	8-16 cores	16-32 GB	1-2 NVIDIA L40S/H100	100 GB

Integration Architecture

These new components integrate with the existing KAI platform through:

1. API Endpoints: REST and gRPC interfaces for service-to-service communication
2. Workflow Templates: Argo Workflows for orchestrating complex ML pipelines
3. Event Streams: Kafka topics for asynchronous communication
4. Shared Storage: Access to common persistent volumes for ML artifacts

Monitoring Recommendations

Key metrics to monitor:

• Notification delivery rates and latencies
• Parameter registry query performance
• GPU utilization across ML workloads
• Model inference latencies
• Workflow completion times

For detailed deployment instructions, configuration options, and rollback procedures, refer to the Deployment Updates document.