platform-java AI Capabilities

Date: 2026-05-28
Status: Planned (Phase 4-5, Month 19+)
Related: VirtOS Issue #128 - AI Architecture Split

Overview

platform-java provides application-level AI capabilities for deploying, managing, and serving AI/ML workloads.

This complements VirtOS's infrastructure-level AI (VM placement, auto-scaling, GPU management) with application orchestration, model serving, and MLOps workflows.

Architecture Split: VirtOS vs platform-java

VirtOS Handles (Infrastructure AI)

AI VM placement (optimal host selection)
Predictive auto-scaling (resource management)
GPU passthrough/vGPU (hardware resources)
Infrastructure security (VM anomaly detection)
Cost optimization (waste detection)
Self-healing VMs (recovery)

See: VirtOS AI_ARCHITECTURE_SPLIT.md

platform-java Handles (Application AI)

MLOps platform (workflow orchestration)
Model marketplace (curated catalog)
LLM serving (inference services)
RAG infrastructure (document Q&A)
Experiment tracking (MLflow integration)
AI governance (compliance, bias testing)
Prompt management (versioning, A/B testing)
Multi-modal AI (vision, speech models)

This Document describes platform-java's application AI capabilities.

Planned Capabilities

1. MLOps Platform (Issue #303)

Purpose: End-to-end ML workflow orchestration

Features:

Experiment tracking (MLflow integration)
Model training orchestration
Distributed training support
Model registry and versioning
A/B testing infrastructure
Deployment automation
Performance monitoring

Example:

# Create ML project
platform-java ml project create fraud-detection

# Train model
platform-java ml train \
  --model fraud-detection \
  --data s3://training-data/ \
  --framework pytorch \
  --gpus 4

# Track experiments
platform-java ml experiments list

# Deploy best model
platform-java ml deploy fraud-detection-v2 --production

Timeline: Phase 4 (Month 19-24), 20 weeks effort

2. Model Marketplace (Issue #304)

Purpose: Curated catalog of pre-trained AI models

Features:

Model catalog (LLMs, vision, speech, etc.)
One-click deployment
Version management
Resource optimization (quantization, pruning)
Multi-modal model support
Model performance metrics

Example:

# Browse models
platform-java marketplace list

# Deploy LLM
platform-java marketplace deploy llama-3.1-70b \
  --optimization fp16 \
  --gpus 2 \
  --auto-scale

# Deploy vision model
platform-java marketplace deploy yolov8 \
  --task object-detection \
  --optimization tensorrt

Available Models:

LLMs: Llama 3.1, Mistral, Gemma
Vision: YOLO, Stable Diffusion, CLIP
Speech: Whisper, TTS models
Embeddings: all-MiniLM, BGE, E5

Timeline: Phase 4 (Month 19-24), 14 weeks effort

3. RAG Infrastructure (Issue #305)

Purpose: Retrieval Augmented Generation platform

Features:

Vector database integration (Qdrant, Weaviate, Milvus)
Document ingestion pipeline
Embedding generation
LLM integration
Query API
Web UI

Example:

# Create RAG project
platform-java rag project create internal-docs

# Ingest documents
platform-java rag ingest \
  --source /path/to/docs \
  --embedding all-MiniLM-L6-v2 \
  --chunk-size 512

# Query
platform-java rag query "How do I configure high availability?"

# Returns:
# Answer: To configure HA, use virtos-ha command...
# Sources:
#   - docs/HA_SETUP.md (lines 45-67)
#   - docs/CLUSTERING.md (lines 120-145)
# Confidence: 94%

Timeline: Phase 5 (Year 3), 18 weeks effort

4. LLM Serving

Purpose: Production-grade LLM inference

Features:

vLLM integration (fast inference)
Text Generation Inference (TGI)
OpenAI API compatibility
Request batching
Token streaming
Rate limiting
Cost tracking

Example:

# Deploy LLM inference service
platform-java llm serve llama-3.1-70b \
  --gpus 2 \
  --max-batch-size 32 \
  --api openai-compatible

# Use with OpenAI SDK
export OPENAI_API_BASE=http://platform-java.local/v1
python my_openai_app.py  # Works unchanged

5. Experiment Tracking

Purpose: Track ML experiments and compare results

Features:

MLflow integration
Experiment versioning
Parameter tracking
Metric comparison
Artifact storage
Model promotion

Example:

# List experiments
platform-java ml experiments list

# Compare runs
platform-java ml experiments compare \
  fraud-detection-v1 \
  fraud-detection-v2

# Output:
#   v1: Accuracy 94.2%, F1 0.91
#   v2: Accuracy 96.1%, F1 0.94  ⭐ Best

# Promote to production
platform-java ml promote fraud-detection-v2 --to production

6. AI Governance & Compliance

Purpose: Ensure responsible AI usage

Features:

Model versioning and lineage
Bias testing and detection
Explainability (SHAP, LIME)
Audit logging
Privacy controls (differential privacy)
Compliance reporting

Example:

# Run governance check
platform-java ai governance-check

# Output:
#   ✅ Model Versioning: All models tracked
#   ✅ Data Lineage: Training data documented
#   ⚠️  Bias Testing: fraud-detection not tested
#   ⚠️  Explainability: No SHAP/LIME integration
#   ✅ Privacy: Differential privacy enabled
#   
#   Compliance Status: 70% (needs improvement)

7. Prompt Management

Purpose: Version control and testing for prompts

Features:

Prompt versioning
A/B testing
Performance tracking
Template library
Multi-language support

Example:

# Create prompt
platform-java ai prompts create customer-support \
  --template "You are a helpful customer support agent..." \
  --version 1.0

# A/B test prompts
platform-java ai prompts test \
  --variants customer-support-v1,customer-support-v2 \
  --metric satisfaction-score

# Results:
#   v1: 4.2/5 satisfaction
#   v2: 4.7/5 satisfaction ⭐ Winner

# Promote winner
platform-java ai prompts promote customer-support-v2

8. Multi-Modal AI

Purpose: Deploy vision, speech, and text models

Features:

Vision models (object detection, image generation)
Speech models (STT, TTS)
Multi-modal models (CLIP, vision-language)
Unified API

Example:

# Deploy vision model
platform-java marketplace deploy yolov8 \
  --task object-detection

# Deploy speech model
platform-java marketplace deploy whisper-large \
  --task transcription \
  --languages en,es,fr

# Deploy text-to-speech
platform-java marketplace deploy tts-1 \
  --voices alloy,echo,fable

Integration with VirtOS

platform-java requests infrastructure from VirtOS and deploys applications on top.

Example: Deploy LLM

Flow:

User → platform-java:

platform-java marketplace deploy llama-3.1-70b

platform-java → VirtOS (REST API):

POST /api/v1/vms
{
  "name": "llm-inference-1",
  "gpu": "nvidia-a100",
  "cpu": 8,
  "ram": 32768,
  "ai_placement": true
}

VirtOS (infrastructure AI):
- Analyzes available hosts
- Selects optimal placement
- Creates VM with GPU
- Returns VM details
platform-java (application AI):
- Deploys Llama model on VM
- Configures inference server (vLLM)
- Sets up monitoring
- Returns API endpoint

Result: User gets working LLM endpoint, VirtOS handled infrastructure, platform-java handled application.

API Contracts

platform-java → VirtOS

Request GPU VM:

POST /api/v1/vms
{
  "name": "llm-inference-1",
  "gpu": "nvidia-a100",
  "cpu": 8,
  "ram": 32768,
  "ai_placement": true,
  "optimization": "ml-inference"
}

VirtOS Response:

{
  "vm_id": "vm-12345",
  "host": "virtos-node-3",
  "ip": "192.168.1.50",
  "gpu_device": "0000:81:00.0",
  "placement_confidence": 0.94
}

Implementation Timeline

Phase 4 (Month 19-24) - Core AI Platform

Focus: MLOps and model deployment

MLOps Platform Basics (Issue #303)
- Experiment tracking (MLflow)
- Model registry
- Deployment automation
- Effort: 20 weeks
Model Marketplace (Issue #304)
- Curated model catalog
- One-click deployment
- LLM serving (vLLM, TGI)
- Effort: 14 weeks

Deliverable: Users can deploy and manage AI/ML models

Phase 5 (Year 3) - Advanced Capabilities

Focus: RAG, governance, multi-modal

RAG Infrastructure (Issue #305)
- Vector database integration
- Document Q&A platform
- Effort: 18 weeks
AI Governance
- Bias testing
- Explainability
- Compliance reporting
- Effort: 12 weeks
Multi-Modal Support
- Vision models
- Speech models
- Unified API
- Effort: 10 weeks

Deliverable: Enterprise-grade AI platform

Technology Stack

Core Technologies

Java 21+ - Platform runtime
Spring Boot - Application framework
MLflow - Experiment tracking
vLLM - LLM inference
Qdrant/Weaviate - Vector databases
PyTorch/TensorFlow - Model frameworks

Integration

VirtOS REST API - Infrastructure requests
Docker/Podman - Container runtime
Kubernetes (optional) - Orchestration
OpenAI API - Compatibility layer

Benefits

For Users

✅ Easy deployment - One command to deploy models
✅ Curated catalog - Pre-tested, optimized models
✅ Production-ready - Monitoring, scaling, governance
✅ Cost-effective - Optimal resource usage
✅ Flexible - Works on VirtOS, VMware, cloud, bare metal

For VirtOS

✅ Clear separation - VirtOS handles infrastructure only
✅ Independent evolution - platform-java can advance separately
✅ Lightweight - No Java/ML dependencies in VirtOS
✅ Modular - Use platform-java or not, your choice

For platform-java

✅ Focused scope - Application AI only
✅ Rich ecosystem - Java ML libraries (DL4J, DJL)
✅ Cloud-ready - Not tied to one hypervisor
✅ Extensible - Plugin-based architecture

Getting Started (Future)

When implemented (Phase 4+):

# Install platform-java
curl -sSL https://platform-java.io/install.sh | bash

# Configure VirtOS backend
platform-java config set virtos.api http://virtos.local/api/v1

# Deploy your first model
platform-java marketplace deploy llama-3.1-8b

# Query the model
curl http://platform-java.local/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Hello!", "max_tokens": 50}'

Created: 2026-05-28
Status: Planned (not yet implemented)
Timeline: Phase 4-5 (Month 19+, Year 3)
Issues: #303 (MLOps), #304 (Marketplace), #305 (RAG)

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platform-java AI Capabilities

Overview

Architecture Split: VirtOS vs platform-java

VirtOS Handles (Infrastructure AI)

platform-java Handles (Application AI)

Planned Capabilities

1. MLOps Platform (Issue #303)

2. Model Marketplace (Issue #304)

3. RAG Infrastructure (Issue #305)

4. LLM Serving

5. Experiment Tracking

6. AI Governance & Compliance

7. Prompt Management

8. Multi-Modal AI

Integration with VirtOS

Example: Deploy LLM

API Contracts

platform-java → VirtOS

Implementation Timeline

Phase 4 (Month 19-24) - Core AI Platform

Phase 5 (Year 3) - Advanced Capabilities

Technology Stack

Core Technologies

Integration

Benefits

For Users

For VirtOS

For platform-java

Related Documentation

Getting Started (Future)

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platform-java AI Capabilities

Overview

Architecture Split: VirtOS vs platform-java

VirtOS Handles (Infrastructure AI)

platform-java Handles (Application AI)

Planned Capabilities

1. MLOps Platform (Issue #303)

2. Model Marketplace (Issue #304)

3. RAG Infrastructure (Issue #305)

4. LLM Serving

5. Experiment Tracking

6. AI Governance & Compliance

7. Prompt Management

8. Multi-Modal AI

Integration with VirtOS

Example: Deploy LLM

API Contracts

platform-java → VirtOS

Implementation Timeline

Phase 4 (Month 19-24) - Core AI Platform

Phase 5 (Year 3) - Advanced Capabilities

Technology Stack

Core Technologies

Integration

Benefits

For Users

For VirtOS

For platform-java

Related Documentation

Getting Started (Future)