Epic: AiDotNet Platform Integration - Model Metadata, Licensing, Hub & API

Open ooples opened this issue 2 months ago • 0 comments

AiDotNet Platform Integration - Epic

Executive Summary

Transform AiDotNet from a library-only solution into a complete platform ecosystem that enables web-based model creation, deployment, and monetization through a "Lovable for AI Models" experience.

Business Value:

Enable non-technical users to create AI models through natural language
Monetize pre-trained models through license verification
Create recurring revenue through hosted API inference
Build a model marketplace ecosystem
Lower barrier to entry for ML adoption

Timeline: 28 weeks (~7 months) Priority: High - Strategic platform initiative

Documents

📄 Complete Specification: See PLATFORM_INTEGRATION_USER_STORY.md (100+ pages of detailed technical specs) 📊 Gap Analysis: See PLATFORM_INTEGRATION_GAP_ANALYSIS.md (Gemini AI analysis identifying critical gaps)

Phases Overview

Phase 1: Model Metadata Foundation (Weeks 1-3)

Goal: Enable models to be loaded without manual type specification

User Stories:

US 1.1: Serialization Format with Type Metadata
US 1.2: Model Type Registry Pattern
US 1.3: IServableModel<T> Interface Definition (NEW - from gap analysis)
US 1.4: Dynamic Shape Support (NEW - from gap analysis)

Key Deliverables:

Model files include JSON headers with type metadata
Factory pattern for extensible model loading
Backward compatibility with legacy models
Migration utility for existing models

Acceptance Criteria:

✅ Models save with complete metadata headers
✅ LoadModel endpoint automatically instantiates correct model types
✅ Legacy models still load successfully
✅ < 1ms overhead for metadata read/write

Phase 2: License Verification System (Weeks 4-6)

Goal: Monetize premium models through cryptographic license verification

User Stories:

US 2.1: License Key Validation Service
US 2.2: License Key Revocation Mechanism (NEW)
US 2.3: Secrets Management Integration (NEW)

Key Deliverables:

Online and offline license verification
License server API with PostgreSQL backend
Cryptographically signed license keys (Ed25519)
Rate limiting and abuse prevention
Cached verification (1-hour TTL)

Acceptance Criteria:

✅ Premium models require valid licenses
✅ License verification < 100ms (online), < 1ms (cached)
✅ Compromised keys can be revoked in real-time
✅ Usage limits enforced per license tier

Phase 3: Model Hub Integration (Weeks 7-9)

Goal: Enable users to download pre-trained models from centralized hub

User Stories:

US 3.1: Model Hub Client
US 3.2: Model Security Scanning (NEW)
US 3.3: Download Resumption Implementation (NEW)

Key Deliverables:

REST API client for hub.aidotnet.com
Model search and discovery
Download with progress tracking
Checksum verification
CLI tool for model management

Acceptance Criteria:

✅ Search models by category, task, license
✅ Download with resume support
✅ Models scanned for malicious code before publishing
✅ Checksum verified automatically after download

Phase 4: Platform API for Model Creation (Weeks 10-13)

Goal: Enable web-based AI model creation via natural language

User Stories:

US 4.1: Web-Based Model Creation API
US 4.2: NLP Model Description Parser (NEW - Critical)
US 4.3: Training Orchestration Service (NEW - Critical)
US 4.4: Usage Tracking and Reporting (NEW)

Key Deliverables:

Natural language → model configuration parser
Async training job management
WebSocket real-time progress updates
Model deployment automation
Multi-tenant inference API

Acceptance Criteria:

✅ Users create models from plain English descriptions
✅ Training jobs tracked with real-time progress
✅ Models deployed with auto-generated API endpoints
✅ Rate limiting per tier enforced
✅ Usage tracked for billing

CRITICAL GAPS IDENTIFIED:

⚠️ NLP parser implementation must be specified (GPT-4 API or Llama 2)
⚠️ Training orchestrator needs detailed design (Horovod/Ray)
⚠️ Platform API ↔ Core Library integration needs specification

Phase 5: Essential Infrastructure (Weeks 14-18) ⭐ NEW

Goal: Implement missing foundational systems identified in gap analysis

User Stories:

US 5.1: User Management System (Identity Server 4 / Auth0)
US 5.2: Billing Integration (Stripe webhooks)
US 5.3: Dataset Management System (Upload, storage, validation)
US 5.4: Notification Service (SendGrid + SignalR)
US 5.5: Audit Logging Service (Event sourcing)
US 5.6: API Gateway (Kong / Azure API Management)
US 5.7: Secrets Management (Azure Key Vault / HashiCorp Vault)
US 5.8: CI/CD Pipeline (GitHub Actions)
US 5.9: Disaster Recovery (Automated backups, restore testing)

Key Deliverables:

Complete user registration/authentication system
Subscription and usage-based billing
Dataset upload/storage with validation
Email and WebSocket notifications
Comprehensive audit trail for compliance
Unified API gateway for external access
Secure secrets management
Automated deployment pipelines
Daily backups with monthly restore tests

CRITICAL - BLOCKING ITEMS:

🚨 Cannot proceed with user-facing features without User Management
🚨 Cannot monetize without Billing Integration
🚨 Cannot train models without Dataset Management
🚨 Cannot deploy to production without Secrets Management & DR

Phase 6: Frontend Development (Weeks 19-24) ⭐ NEW

Goal: Build web interface for "Lovable for AI Models" experience

User Stories:

US 6.1: Web Application Architecture (React / Blazor)
US 6.2: Model Creation UI (NL input, visual builder)
US 6.3: Model Hub UI (Browse, search, download)
US 6.4: Dashboard UI (Usage, costs, metrics)
US 6.5: User Settings UI (Profile, billing, API keys)

Key Deliverables:

Responsive web application
Natural language model creation interface
Visual model hub browser
Real-time usage and cost dashboard
User profile and settings management

Design Requirements:

Wireframes and user flows
Design system (colors, typography, components)
Accessibility (WCAG 2.1 Level AA)
Mobile-responsive
Interactive tutorials for onboarding

Phase 7: Production Hardening (Weeks 25-28) ⭐ NEW

Goal: Ensure system is secure, reliable, and production-ready

User Stories:

US 7.1: Security Audit & Penetration Testing
US 7.2: Performance Optimization & Load Testing
US 7.3: Chaos Engineering & Resilience Testing
US 7.4: Documentation Completion
US 7.5: User Acceptance Testing

Key Deliverables:

External security audit report
Load test results (1M+ inferences/sec)
Chaos engineering test results
Complete API documentation (OpenAPI)
User manuals and guides
UAT with beta users

Success Metrics:

✅ 99.9% uptime SLA
✅ < 100ms inference latency (p95)
✅ Pass penetration testing
✅ < 5 seconds model loading time
✅ 80%+ user satisfaction in UAT

Gap Analysis Summary

Critical Gaps (P0 - Blocking):

IServableModel<T> interface not defined - BLOCKING ALL PHASES
NLP parser implementation not specified - BLOCKING PHASE 4
User Management System missing - BLOCKING ALL USER FEATURES
Dataset Management System missing - BLOCKING TRAINING
Frontend architecture not defined - BLOCKING PLATFORM LAUNCH
GDPR compliance not addressed - BLOCKING EU MARKET
Secrets management not specified - BLOCKING PRODUCTION
Disaster recovery not planned - BLOCKING PRODUCTION

High Priority Gaps (P1):

Billing integration details incomplete
License revocation mechanism missing
Platform API ↔ Library integration unclear
Multiple security gaps (input validation, model isolation, incident response)
Operational gaps (CI/CD, resource management, rollback strategy)

See full gap analysis document for complete details.

Dependencies & Technology Choices

Must Decide Before Implementation:

NLP/AI:

OpenAI GPT-4 API
Azure OpenAI Service
Fine-tuned Llama 2

Message Broker:

RabbitMQ
Apache Kafka
Azure Service Bus

Object Storage:

Azure Blob Storage
AWS S3
Google Cloud Storage

Monitoring:

Prometheus + Grafana
Datadog
New Relic

Frontend Framework:

React + TypeScript
Blazor WebAssembly
Next.js

Success Metrics

Adoption:

Models created per month
Active API users
Model hub downloads

Revenue:

Monthly recurring revenue (MRR)
Average revenue per user (ARPU)
License conversion rate

Technical:

API uptime (target: 99.9%)
Inference latency (target: < 100ms p95)
Training job success rate (target: > 95%)

User Experience:

Time from description to deployed model (target: < 10 minutes)
User satisfaction score (target: 4.5/5)
Support ticket volume (target: < 5% of active users)

Risks & Mitigations

Risk	Impact	Probability	Mitigation
NLP parser fails on ambiguous input	High	High	Add clarification prompts, templates
Training jobs fail frequently	High	Medium	Checkpointing, retry logic, user notifications
License server downtime	High	Medium	Offline verification, caching, 3-nines SLA
API abuse / DDoS	Medium	High	Rate limiting, Cloudflare protection
Data privacy breach	Critical	Low	Encryption, audit logging, penetration testing
Runaway cloud costs	High	Medium	Resource quotas, cost alerts, auto-scaling limits

Open Questions

NLP Implementation: GPT-4 API ($$$) vs. fine-tuned open-source model (complexity)?
Cloud Provider: Azure, AWS, or GCP for primary deployment?
Pricing Strategy: Tiered pricing amounts? Free tier limits?
Model Marketplace: Allow third-party model publishing? Revenue sharing?
On-Premise: Support enterprise on-premise deployments?
Data Residency: Multi-region for EU data residency requirements?

Next Steps

Immediate Actions (This Week):

✅ Create this GitHub issue
⏳ Schedule architecture review meeting
⏳ Make technology stack decisions (NLP, cloud, frameworks)
⏳ Define IServableModel<T> interface (blocking Phase 1)
⏳ Create detailed Phase 1 implementation tasks

Short-Term (Next 2 Weeks):

Break down Phase 1 into individual GitHub issues
Set up development environment
Create project board for tracking
Assign team members to phases
Begin Phase 1 implementation

Before Starting Phase 4:

Finalize NLP parser implementation approach
Design training orchestrator architecture
Define Platform API ↔ Library integration
Complete Phase 1-3 and validate

Before Production Launch:

Complete all 7 phases
Address all P0 and P1 gaps from gap analysis
Pass security audit and penetration testing
Complete UAT with beta users
Finalize pricing and billing integration

Related Issues

#380 - AiDotNet.Serving improvements (foundational work)
#308 - Model Serving Framework implementation

Documentation References

PLATFORM_INTEGRATION_USER_STORY.md - Complete 100+ page technical specification
PLATFORM_INTEGRATION_GAP_ANALYSIS.md - Comprehensive gap analysis by Gemini AI

Status: Draft - Ready for Architecture Review Estimated Effort: 28 weeks with dedicated team Dependencies: PR #380 must merge first

Nov 07 '25 16:11 ooples