ISSUE_TEMPLATE_ML_DECISION_ENGINE_PRODUCTION.md

Issue Template: ML Decision Engine Production Implementation

File: /skz-integration/autonomous-agents-framework/src/models/ml_decision_engine.py
Priority: High
Estimated Time: 4-6 weeks
Assigned Team: ML/AI Specialist Team

---

📋 CURRENT MOCK IMPLEMENTATIONS TO REPLACE

1. Simple Text Classification (Lines 88-98)

def classify_text(self, text: str, categories: List[str]) -> Dict[str, float]:
    # Simple keyword-based classification
    category_scores = {}
    for category in categories:
        category_keywords = self._get_category_keywords(category)
        score = sum(1 for keyword in category_keywords if keyword.lower() in text.lower())
        category_scores[category] = score / len(category_keywords) if category_keywords else 0
    
    return category_scores

2. Basic Quality Assessment (Lines 174-191)

def assess_quality(self, manuscript: Dict[str, Any]) -> Dict[str, Any]:
    # Basic feature extraction without real ML
    features = self.extract_features(manuscript)
    features_scaled = self.scaler.transform(features)
    quality_score = self.quality_model.predict_proba(features_scaled)[0][1]
    # ... simplified quality breakdown

---

🎯 PRODUCTION IMPLEMENTATION REQUIREMENTS

Task 1: BERT-Based Text Classification System

Estimated Time: 2 weeks

Prerequisites:

• Set up GPU-enabled infrastructure for model inference
• Install PyTorch/TensorFlow and transformers library
• Download and test pre-trained BERT models
• Set up model versioning and storage system

Implementation Tasks:

• Replace keyword-based classification with BERT embeddings
• Implement production ProductionNLPProcessor class
• Add semantic similarity calculations using sentence transformers
• Implement fine-tuned domain-specific classifiers
• Add ONNX runtime for optimized inference
• Implement model caching and batch processing
• Add comprehensive error handling and fallbacks
• Create unit tests for all ML components

Code Template:

class ProductionNLPProcessor:
    def __init__(self, model_config):
        # Load pre-trained models
        self.bert_model = transformers.AutoModel.from_pretrained(
            model_config.get('bert_model', 'bert-base-uncased')
        )
        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
            model_config.get('bert_model', 'bert-base-uncased')
        )
        
        # Load fine-tuned classification models
        self.classification_models = {}
        for domain in ['cosmetic_chemistry', 'dermatology', 'toxicology']:
            model_path = model_config.get(f'{domain}_classifier')
            if model_path and os.path.exists(model_path):
                self.classification_models[domain] = joblib.load(model_path)
        
        # Initialize ONNX runtime for fast inference
        import onnxruntime as ort
        self.ort_session = ort.InferenceSession(
            model_config.get('onnx_model_path')
        ) if model_config.get('onnx_model_path') else None

    async def classify_text(self, text: str, categories: List[str]) -> Dict[str, float]:
        """Production text classification using BERT + fine-tuned classifiers"""
        try:
            # Tokenize input text
            inputs = self.tokenizer(
                text,
                padding=True,
                truncation=True,
                max_length=512,
                return_tensors="pt"
            )
            
            # Get BERT embeddings
            with torch.no_grad():
                outputs = self.bert_model(**inputs)
                embeddings = outputs.last_hidden_state.mean(dim=1)
            
            category_scores = {}
            
            for category in categories:
                if category in self.classification_models:
                    # Use fine-tuned model for specific categories
                    model = self.classification_models[category]
                    score = model.predict_proba(embeddings.numpy())[0][1]
                    category_scores[category] = float(score)
                else:
                    # Use semantic similarity for unknown categories
                    category_embedding = await self._get_category_embedding(category)
                    similarity = torch.cosine_similarity(embeddings, category_embedding)
                    category_scores[category] = float(similarity.item())
            
            # Normalize scores
            total_score = sum(category_scores.values())
            if total_score > 0:
                category_scores = {k: v/total_score for k, v in category_scores.items()}
            
            return category_scores
            
        except Exception as e:
            logger.error(f"Text classification error: {e}")
            return await self._fallback_classify_text(text, categories)

Task 2: Advanced Quality Assessment Pipeline

Estimated Time: 1.5 weeks

Implementation Tasks:

• Implement ensemble model for manuscript quality prediction
• Add BERT-based content quality assessment
• Implement statistical feature quality analysis
• Add writing quality assessment using language models
• Implement novelty detection using citation analysis
• Create feature extraction pipeline for quality metrics
• Add model training capabilities for quality assessment
• Implement quality prediction confidence intervals

Code Template:

async def predict_manuscript_quality(self, manuscript_text: str) -> Dict[str, float]:
    """Production quality prediction using ensemble models"""
    try:
        # Extract features
        features = await self._extract_quality_features(manuscript_text)
        
        # Use ensemble of models for prediction
        quality_predictions = {}
        
        # BERT-based content quality
        content_score = await self._predict_content_quality(manuscript_text)
        quality_predictions['content_quality'] = content_score
        
        # Statistical features quality
        statistical_score = await self._predict_statistical_quality(features)
        quality_predictions['statistical_quality'] = statistical_score
        
        # Writing quality assessment
        writing_score = await self._predict_writing_quality(manuscript_text)
        quality_predictions['writing_quality'] = writing_score
        
        # Novelty assessment
        novelty_score = await self._predict_novelty(manuscript_text)
        quality_predictions['novelty'] = novelty_score
        
        # Ensemble prediction
        overall_score = np.mean(list(quality_predictions.values()))
        quality_predictions['overall_quality'] = overall_score
        
        return quality_predictions
        
    except Exception as e:
        logger.error(f"Quality prediction error: {e}")
        return {'overall_quality': 0.5, 'error': str(e)}

Task 3: Model Training and Fine-tuning Framework

Estimated Time: 1.5 weeks

Implementation Tasks:

• Create training data pipeline for domain-specific models
• Implement model fine-tuning for cosmetic science domain
• Add model evaluation and validation frameworks
• Implement automated retraining pipelines
• Add model versioning and A/B testing capabilities
• Create model performance monitoring
• Implement data drift detection
• Add model explainability features

Task 4: Production Model Deployment Infrastructure

Estimated Time: 1 week

Implementation Tasks:

• Set up MLflow for model management and deployment
• Implement ONNX model conversion for production inference
• Add model serving with REST API endpoints
• Implement model load balancing and scaling
• Add GPU memory management and optimization
• Create model health checks and monitoring
• Implement model rollback capabilities
• Add performance benchmarking and optimization

---

🔧 TECHNICAL SPECIFICATIONS

Model Configuration:

ML_CONFIG = {
    'bert_model': 'sentence-transformers/allenai-specter',  # Scientific papers
    'device': 'cuda' if torch.cuda.is_available() else 'cpu',
    'max_sequence_length': 512,
    'batch_size': 32,
    'model_cache_dir': '/models/cache',
    'onnx_model_path': '/models/production/text_classifier.onnx',
    
    # Domain-specific classifiers
    'cosmetic_chemistry_classifier': '/models/cosmetic_chemistry_bert.pkl',
    'dermatology_classifier': '/models/dermatology_bert.pkl',
    'toxicology_classifier': '/models/toxicology_bert.pkl',
    
    # Quality assessment models
    'quality_ensemble_models': [
        '/models/content_quality_bert.pkl',
        '/models/statistical_quality_rf.pkl',
        '/models/writing_quality_gpt.pkl'
    ],
    
    # Performance settings
    'inference_timeout': 10.0,  # seconds
    'max_concurrent_requests': 100,
    'model_memory_limit': '8GB'
}

Dependencies to Add:

# Add to requirements.txt
torch>=1.13.0
transformers>=4.21.0
sentence-transformers>=2.2.0
onnxruntime>=1.12.0
mlflow>=2.0.0
scikit-learn>=1.1.0
numpy>=1.21.0
pandas>=1.5.0
spacy>=3.4.0
nltk>=3.7
textstat>=0.7.0

GPU Infrastructure Requirements:

• Minimum: 1x NVIDIA Tesla T4 (16GB VRAM)
• Recommended: 1x NVIDIA A100 (40GB VRAM)
• CPU: 8+ cores for model preprocessing
• RAM: 32GB+ for model loading and caching
• Storage: 100GB+ SSD for model storage

---

🧪 TESTING REQUIREMENTS

Unit Tests:

• Test BERT model loading and inference
• Test text tokenization and preprocessing
• Test semantic similarity calculations
• Test quality assessment feature extraction
• Test ensemble model predictions
• Test model fallback mechanisms
• Test error handling for GPU/CPU switching

Integration Tests:

• Test end-to-end text classification pipeline
• Test quality assessment with real manuscript data
• Test model training and fine-tuning workflows
• Test ONNX model conversion and inference
• Test model serving API endpoints
• Test concurrent model inference under load

Performance Tests:

• Benchmark inference time for different text lengths
• Test memory usage under concurrent requests
• Test GPU utilization and optimization
• Test model caching effectiveness
• Test batch processing performance

Model Validation Tests:

• Validate classification accuracy on test datasets
• Test model performance on domain-specific texts
• Validate quality assessment predictions
• Test model bias and fairness metrics
• Validate model explainability outputs

---

📈 SUCCESS CRITERIA

Performance Metrics:

• Classification Accuracy: > 90% on domain-specific test sets
• Inference Time: < 100ms per classification request
• Throughput: 1000+ classifications per minute
• Model Availability: > 99.9% uptime
• GPU Utilization: > 80% efficient usage

Quality Metrics:

• Model Precision: > 0.85 for all classification categories
• Model Recall: > 0.80 for all classification categories
• Quality Assessment Correlation: > 0.75 with human evaluators
• Prediction Confidence: Calibrated confidence scores

---

🚨 RISK MITIGATION

Potential Risks:

1. Model Drift: Performance degradation over time

   • Mitigation: Implement automated retraining and monitoring

2. GPU Memory Issues: Out-of-memory errors during inference

   • Mitigation: Implement dynamic batching and memory management

3. Model Bias: Unfair predictions for certain manuscript types

   • Mitigation: Regular bias audits and diverse training data

4. Inference Latency: Slow response times under load

   • Mitigation: ONNX optimization and efficient caching

---

📚 DOCUMENTATION REQUIREMENTS

Technical Documentation:

• Model architecture and design decisions
• Training data requirements and preprocessing
• Model deployment and serving guide
• Performance optimization guide
• Model monitoring and maintenance procedures

API Documentation:

• Classification API endpoint documentation
• Quality assessment API documentation
• Model management API documentation
• Error codes and troubleshooting guide

---

✅ ACCEPTANCE CRITERIA

• Simple keyword classification replaced with BERT-based system
• Basic quality assessment replaced with ensemble ML models
• Production-grade model serving infrastructure deployed
• Model training and fine-tuning framework operational
• ONNX optimization for production inference implemented
• All tests passing with >90% model accuracy
• Performance targets met in production environment
• Model monitoring and alerting systems operational
• Documentation complete and API endpoints documented
• GPU infrastructure optimized and cost-effective
• Model bias and fairness audits completed
• Production deployment successful and stable

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Issue Created: {timestamp}
Last Updated: {timestamp}
Status: Open
Labels: critical, production, machine-learning, bert, nlp, high-priority