CLAUDE MD AI ML Projects

Claude Code Configuration for AI/ML Projects

🚨 CRITICAL: PARALLEL ML PIPELINE EXECUTION

MANDATORY RULE: In ML projects, ALL pipeline operations MUST be parallel:

Data preprocessing → Parallel feature engineering across datasets
Model training → Concurrent training of multiple architectures
Hyperparameter tuning → Distributed grid/random search
Model evaluation → Parallel validation across metrics
MLOps deployment → Concurrent staging and production updates

🧠 AI/ML AGENT SPECIALIZATION

Required Agent Types for ML Projects:

Data Engineer - ETL pipelines, feature stores, data validation
ML Researcher - Model architecture, research paper analysis
Model Trainer - Training loops, hyperparameter optimization
MLOps Engineer - Deployment, monitoring, CI/CD for models
Data Scientist - Statistical analysis, feature engineering
Model Validator - Testing, validation, A/B testing
Performance Monitor - Model drift, performance tracking

🚀 ML SWARM INITIALIZATION PATTERN

Standard ML Project Setup:

// ✅ CORRECT: Parallel ML swarm initialization
[BatchTool - Message 1]:
  // Initialize ML-focused swarm
  mcp__claude-flow__swarm_init { 
    topology: "hierarchical", 
    maxAgents: 8, 
    strategy: "ml_pipeline" 
  }

  // Spawn ALL ML agents in parallel
  mcp__claude-flow__agent_spawn { type: "researcher", name: "ML Researcher", capabilities: ["paper_analysis", "architecture_design"] }
  mcp__claude-flow__agent_spawn { type: "coder", name: "Data Engineer", capabilities: ["etl", "feature_engineering", "data_validation"] }
  mcp__claude-flow__agent_spawn { type: "specialist", name: "Model Trainer", capabilities: ["pytorch", "tensorflow", "hyperparameter_tuning"] }
  mcp__claude-flow__agent_spawn { type: "analyst", name: "Data Scientist", capabilities: ["statistics", "visualization", "feature_selection"] }
  mcp__claude-flow__agent_spawn { type: "coder", name: "MLOps Engineer", capabilities: ["deployment", "monitoring", "ci_cd"] }
  mcp__claude-flow__agent_spawn { type: "tester", name: "Model Validator", capabilities: ["testing", "validation", "ab_testing"] }
  mcp__claude-flow__agent_spawn { type: "monitor", name: "Performance Monitor", capabilities: ["drift_detection", "metrics"] }
  mcp__claude-flow__agent_spawn { type: "coordinator", name: "ML Lead", capabilities: ["coordination", "strategy"] }

  // Orchestrate ML pipeline
  mcp__claude-flow__task_orchestrate { 
    task: "End-to-end ML pipeline development", 
    strategy: "parallel",
    dependencies: ["data_validation", "model_training", "deployment"]
  }

  // Store ML project context
  mcp__claude-flow__memory_usage { 
    action: "store", 
    key: "ml_project/context", 
    value: { 
      "project_type": "ml_pipeline",
      "frameworks": ["pytorch", "tensorflow", "scikit-learn"],
      "deployment_target": "cloud",
      "data_sources": ["s3", "databases", "apis"]
    }
  }

📊 ML PIPELINE TODOWRITE PATTERN

ML-Specific Todo Categories:

// ✅ MANDATORY: All ML todos in ONE call
TodoWrite { todos: [
  // Data Engineering Phase
  { id: "data_ingestion", content: "Setup data ingestion pipelines", status: "in_progress", priority: "high" },
  { id: "data_validation", content: "Implement data quality checks", status: "pending", priority: "high" },
  { id: "feature_engineering", content: "Build feature transformation pipeline", status: "pending", priority: "high" },
  { id: "data_versioning", content: "Setup DVC for data versioning", status: "pending", priority: "medium" },
  
  // Model Development Phase
  { id: "baseline_model", content: "Train baseline model", status: "pending", priority: "high" },
  { id: "hyperparameter_tuning", content: "Optimize hyperparameters", status: "pending", priority: "high" },
  { id: "model_selection", content: "Compare model architectures", status: "pending", priority: "high" },
  { id: "cross_validation", content: "Implement k-fold validation", status: "pending", priority: "medium" },
  
  // MLOps Phase
  { id: "model_registry", content: "Setup model registry", status: "pending", priority: "medium" },
  { id: "ci_cd_pipeline", content: "Build ML CI/CD pipeline", status: "pending", priority: "high" },
  { id: "monitoring_setup", content: "Implement model monitoring", status: "pending", priority: "high" },
  { id: "ab_testing", content: "Setup A/B testing framework", status: "pending", priority: "medium" },
  
  // Deployment Phase
  { id: "containerization", content: "Containerize model inference", status: "pending", priority: "high" },
  { id: "api_endpoints", content: "Build prediction API", status: "pending", priority: "high" },
  { id: "load_testing", content: "Perform inference load testing", status: "pending", priority: "medium" },
  { id: "documentation", content: "Create model documentation", status: "pending", priority: "low" }
]}

🛠️ ML FRAMEWORK INTEGRATION

TensorFlow/Keras Projects:

# Data Agent Coordination Hook
import claude_flow_hooks as cf

@cf.pre_task("tensorflow_training")
def setup_tensorflow_environment():
    """Setup TensorFlow training environment with coordination"""
    cf.memory.store("tf_config", {
        "strategy": "MirroredStrategy",
        "mixed_precision": True,
        "gpu_memory_growth": True
    })

@cf.post_edit("*.py")
def validate_tensorflow_code():
    """Validate TensorFlow code after each edit"""
    cf.hooks.notify("TensorFlow model updated - validating architecture")
    # Run model validation

PyTorch Projects:

# Model Training Agent Coordination
import claude_flow_hooks as cf
import torch
import torch.distributed as dist

@cf.pre_task("pytorch_training")
def setup_distributed_training():
    """Setup PyTorch distributed training"""
    cf.memory.store("pytorch_config", {
        "backend": "nccl",
        "world_size": 4,
        "distributed": True
    })

@cf.post_task("training_epoch")
def log_training_metrics():
    """Log training metrics after each epoch"""
    metrics = cf.memory.retrieve("training_metrics")
    cf.hooks.notify(f"Epoch completed - Loss: {metrics['loss']}")

🔄 ML WORKFLOW ORCHESTRATION

End-to-End ML Pipeline:

// ✅ PARALLEL ML WORKFLOW EXECUTION
[BatchTool - ML Pipeline]:
  // Data preparation phase
  Task("You are Data Engineer. COORDINATE: Run cf.hooks.pre_task('data_prep'). Build ETL pipeline with Pandas, validate data quality with Great Expectations")
  Task("You are Feature Engineer. COORDINATE: Run cf.hooks.pre_task('feature_eng'). Create feature transformation pipeline with sklearn, store features in feature store")
  
  // Model development phase
  Task("You are ML Researcher. COORDINATE: Run cf.hooks.pre_task('research'). Research state-of-art models, implement baseline with TensorFlow/PyTorch")
  Task("You are Model Trainer. COORDINATE: Run cf.hooks.pre_task('training'). Setup distributed training, hyperparameter tuning with Optuna")
  
  // Validation and testing phase
  Task("You are Model Validator. COORDINATE: Run cf.hooks.pre_task('validation'). Implement cross-validation, statistical tests, bias detection")
  Task("You are Performance Tester. COORDINATE: Run cf.hooks.pre_task('perf_test'). Load test inference API, measure latency/throughput")
  
  // Deployment phase
  Task("You are MLOps Engineer. COORDINATE: Run cf.hooks.pre_task('deployment'). Setup MLflow, containerize with Docker, deploy to Kubernetes")
  Task("You are Monitor Agent. COORDINATE: Run cf.hooks.pre_task('monitoring'). Implement model drift detection, performance monitoring with Prometheus")

  // File operations for ML project structure
  Bash("mkdir -p ml_project/{data,models,notebooks,src,tests,configs,deployment}")
  Bash("mkdir -p ml_project/src/{data,features,models,visualization}")
  Bash("mkdir -p ml_project/deployment/{docker,kubernetes,monitoring}")

  // Create ML project files
  Write("ml_project/requirements.txt", ml_requirements)
  Write("ml_project/Dockerfile", dockerfile_content)
  Write("ml_project/src/train.py", training_script)
  Write("ml_project/src/inference.py", inference_script)
  Write("ml_project/configs/model_config.yaml", model_config)

📈 MODEL TRAINING COORDINATION

Distributed Training Pattern:

# training_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class DistributedTrainingCoordinator:
    def __init__(self):
        self.config = cf.memory.retrieve("training_config")
        
    @cf.pre_task("distributed_training")
    def setup_training(self):
        """Coordinate distributed training setup"""
        cf.memory.store("training_status", "initializing")
        cf.hooks.notify("Starting distributed training coordination")
        
    @cf.during_task("training_step")
    def monitor_training(self, step, metrics):
        """Monitor training progress across workers"""
        cf.memory.store(f"step_{step}_metrics", metrics)
        if step % 100 == 0:
            cf.hooks.notify(f"Training step {step} - Loss: {metrics['loss']}")
            
    @cf.post_task("training_complete")
    def finalize_training(self):
        """Finalize and store trained model"""
        cf.memory.store("training_status", "completed")
        cf.hooks.notify("Training completed - model saved to registry")

🔍 MODEL EVALUATION & VALIDATION

Comprehensive Model Testing:

# model_validation_agent.py
import claude_flow_hooks as cf

@cf.coordination_required
class ModelValidationAgent:
    
    @cf.pre_task("model_validation")
    def setup_validation(self):
        """Setup comprehensive model validation"""
        cf.memory.store("validation_config", {
            "cross_validation_folds": 5,
            "test_split": 0.2,
            "metrics": ["accuracy", "precision", "recall", "f1", "auc"]
        })
        
    @cf.parallel_task("validation_metrics")
    def compute_metrics(self):
        """Compute validation metrics in parallel"""
        config = cf.memory.retrieve("validation_config")
        results = {}
        
        # Parallel metric computation
        for metric in config["metrics"]:
            results[metric] = self.compute_metric(metric)
            
        cf.memory.store("validation_results", results)
        cf.hooks.notify(f"Validation complete - Results: {results}")
        
    @cf.post_task("bias_detection")
    def detect_bias(self):
        """Detect model bias across different groups"""
        bias_results = self.analyze_fairness()
        cf.memory.store("bias_analysis", bias_results)
        cf.hooks.notify(f"Bias analysis complete: {bias_results}")

🚀 MLOPS DEPLOYMENT COORDINATION

Kubernetes Deployment Pipeline:

# deployment_coordination.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: ml-coordination-config
data:
  coordination_hooks: |
    pre_deployment:
      - validate_model_metrics
      - run_integration_tests
      - check_resource_requirements
    during_deployment:
      - monitor_deployment_health
      - validate_inference_endpoints
      - run_smoke_tests
    post_deployment:
      - setup_monitoring_alerts
      - log_deployment_success
      - notify_stakeholders

# mlops_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class MLOpsCoordinator:
    
    @cf.pre_task("model_deployment")
    def validate_deployment_readiness(self):
        """Validate model is ready for deployment"""
        model_metrics = cf.memory.retrieve("validation_results")
        if model_metrics["accuracy"] > 0.85:
            cf.memory.store("deployment_approved", True)
            cf.hooks.notify("Model approved for deployment")
        else:
            cf.hooks.notify("Model failed validation threshold")
            
    @cf.during_task("kubernetes_deployment")
    def monitor_deployment(self):
        """Monitor Kubernetes deployment progress"""
        deployment_status = self.check_k8s_status()
        cf.memory.store("k8s_deployment_status", deployment_status)
        cf.hooks.notify(f"K8s deployment status: {deployment_status}")
        
    @cf.post_task("deployment_complete")
    def setup_monitoring(self):
        """Setup model monitoring and alerting"""
        monitoring_config = {
            "drift_detection": True,
            "performance_monitoring": True,
            "alert_thresholds": {"latency": 100, "accuracy": 0.8}
        }
        cf.memory.store("monitoring_config", monitoring_config)
        cf.hooks.notify("Monitoring setup complete")

📊 DATA SCIENCE EXPERIMENT TRACKING

MLflow Integration:

# experiment_tracking.py
import claude_flow_hooks as cf
import mlflow

@cf.coordination_required
class ExperimentTracker:
    
    @cf.pre_task("experiment_start")
    def start_experiment(self, experiment_name):
        """Start MLflow experiment with coordination"""
        mlflow.set_experiment(experiment_name)
        run = mlflow.start_run()
        cf.memory.store("mlflow_run_id", run.info.run_id)
        cf.hooks.notify(f"Started experiment: {experiment_name}")
        
    @cf.during_task("log_metrics")
    def log_experiment_metrics(self, metrics, step=None):
        """Log metrics to MLflow and coordination memory"""
        for metric_name, value in metrics.items():
            mlflow.log_metric(metric_name, value, step)
            
        cf.memory.store(f"metrics_step_{step}", metrics)
        cf.hooks.notify(f"Logged metrics: {metrics}")
        
    @cf.post_task("experiment_complete")
    def finalize_experiment(self):
        """Finalize experiment and store artifacts"""
        run_id = cf.memory.retrieve("mlflow_run_id")
        mlflow.log_artifacts("models/")
        mlflow.end_run()
        
        cf.memory.store("experiment_complete", {
            "run_id": run_id,
            "status": "completed",
            "artifacts_logged": True
        })
        cf.hooks.notify("Experiment completed successfully")

🔐 ML SECURITY & PRIVACY

Privacy-Preserving ML Coordination:

# privacy_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class PrivacyCoordinator:
    
    @cf.pre_task("data_privacy_check")
    def validate_data_privacy(self):
        """Validate data privacy compliance"""
        privacy_config = {
            "pii_detection": True,
            "anonymization": True,
            "differential_privacy": True,
            "consent_tracking": True
        }
        
        cf.memory.store("privacy_config", privacy_config)
        cf.hooks.notify("Privacy validation initiated")
        
    @cf.during_task("differential_privacy")
    def apply_differential_privacy(self, epsilon=1.0):
        """Apply differential privacy to model training"""
        dp_config = {
            "epsilon": epsilon,
            "delta": 1e-5,
            "noise_multiplier": 1.1
        }
        
        cf.memory.store("dp_config", dp_config)
        cf.hooks.notify(f"Differential privacy applied: ε={epsilon}")
        
    @cf.post_task("privacy_audit")
    def conduct_privacy_audit(self):
        """Conduct privacy compliance audit"""
        audit_results = {
            "pii_removed": True,
            "consent_verified": True,
            "differential_privacy": True,
            "audit_timestamp": "2024-01-01T00:00:00Z"
        }
        
        cf.memory.store("privacy_audit", audit_results)
        cf.hooks.notify("Privacy audit completed")

📈 PERFORMANCE MONITORING

Real-time Model Performance Tracking:

# performance_monitor.py
import claude_flow_hooks as cf
from prometheus_client import Gauge, Counter

@cf.coordination_required
class ModelPerformanceMonitor:
    
    def __init__(self):
        # Prometheus metrics
        self.accuracy_gauge = Gauge('model_accuracy', 'Current model accuracy')
        self.prediction_counter = Counter('predictions_total', 'Total predictions made')
        self.drift_gauge = Gauge('model_drift_score', 'Model drift detection score')
        
    @cf.pre_task("monitoring_setup")
    def setup_monitoring(self):
        """Setup monitoring infrastructure"""
        monitoring_config = {
            "drift_detection_window": 1000,
            "performance_check_interval": 3600,
            "alert_thresholds": {
                "accuracy_drop": 0.05,
                "drift_score": 0.3,
                "latency_increase": 0.2
            }
        }
        
        cf.memory.store("monitoring_config", monitoring_config)
        cf.hooks.notify("Performance monitoring initialized")
        
    @cf.during_task("drift_detection")
    def detect_model_drift(self, new_data):
        """Detect model drift from new data"""
        baseline_stats = cf.memory.retrieve("baseline_statistics")
        drift_score = self.calculate_drift(baseline_stats, new_data)
        
        self.drift_gauge.set(drift_score)
        cf.memory.store("current_drift_score", drift_score)
        
        if drift_score > 0.3:
            cf.hooks.notify(f"ALERT: Model drift detected! Score: {drift_score}")
        
    @cf.post_task("performance_report")
    def generate_performance_report(self):
        """Generate comprehensive performance report"""
        metrics = cf.memory.retrieve("performance_metrics")
        report = {
            "accuracy": metrics.get("accuracy", 0),
            "latency_p95": metrics.get("latency_p95", 0),
            "throughput": metrics.get("throughput", 0),
            "drift_score": cf.memory.retrieve("current_drift_score"),
            "uptime": metrics.get("uptime", 0)
        }
        
        cf.memory.store("performance_report", report)
        cf.hooks.notify(f"Performance report generated: {report}")

🧪 A/B TESTING FRAMEWORK

Coordinated A/B Testing for ML Models:

# ab_testing_coordinator.py
import claude_flow_hooks as cf
import random

@cf.coordination_required
class ABTestingCoordinator:
    
    @cf.pre_task("ab_test_setup")
    def setup_ab_test(self, test_config):
        """Setup A/B test configuration"""
        ab_config = {
            "test_name": test_config["name"],
            "control_model": test_config["control_model"],
            "treatment_model": test_config["treatment_model"],
            "traffic_split": test_config.get("traffic_split", 0.5),
            "success_metrics": test_config["success_metrics"],
            "test_duration": test_config.get("duration", 7)  # days
        }
        
        cf.memory.store("ab_test_config", ab_config)
        cf.hooks.notify(f"A/B test setup: {test_config['name']}")
        
    @cf.during_task("traffic_routing")
    def route_traffic(self, user_id):
        """Route traffic between control and treatment models"""
        config = cf.memory.retrieve("ab_test_config")
        
        # Consistent routing based on user_id
        if hash(user_id) % 100 < config["traffic_split"] * 100:
            model_version = config["treatment_model"]
            variant = "treatment"
        else:
            model_version = config["control_model"]
            variant = "control"
            
        # Log routing decision
        cf.memory.store(f"user_{user_id}_variant", variant)
        return model_version, variant
        
    @cf.post_task("ab_test_analysis")
    def analyze_ab_test_results(self):
        """Analyze A/B test results and determine winner"""
        config = cf.memory.retrieve("ab_test_config")
        
        # Statistical analysis
        results = self.perform_statistical_test()
        
        analysis = {
            "test_name": config["test_name"],
            "statistical_significance": results["p_value"] < 0.05,
            "confidence_interval": results["confidence_interval"],
            "winner": results["winner"],
            "lift": results["lift"]
        }
        
        cf.memory.store("ab_test_results", analysis)
        cf.hooks.notify(f"A/B test analysis complete: {analysis}")

🏗️ ML PROJECT STRUCTURE

Standard ML Project Template:

ml_project/
├── data/
│   ├── raw/                 # Raw, immutable data
│   ├── interim/            # Intermediate data
│   ├── processed/          # Final, canonical datasets
│   └── external/           # Third-party data
├── models/
│   ├── trained/            # Trained model artifacts
│   ├── experiments/        # Experiment tracking
│   └── registry/           # Model registry
├── notebooks/
│   ├── exploratory/        # EDA notebooks
│   ├── modeling/           # Model development
│   └── evaluation/         # Model evaluation
├── src/
│   ├── data/               # Data processing scripts
│   ├── features/           # Feature engineering
│   ├── models/             # Model training/prediction
│   ├── visualization/      # Visualization utilities
│   └── utils/              # Utility functions
├── tests/
│   ├── unit/               # Unit tests
│   ├── integration/        # Integration tests
│   └── model/              # Model-specific tests
├── deployment/
│   ├── docker/             # Container definitions
│   ├── kubernetes/         # K8s manifests
│   ├── terraform/          # Infrastructure as code
│   └── monitoring/         # Monitoring configs
├── configs/
│   ├── model/              # Model configurations
│   ├── training/           # Training configurations
│   └── deployment/         # Deployment configurations
└── docs/
    ├── api/                # API documentation
    ├── models/             # Model documentation
    └── deployment/         # Deployment guides

📚 JUPYTER NOTEBOOK COORDINATION

Coordinated Notebook Development:

# notebook_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class NotebookCoordinator:
    
    @cf.pre_task("notebook_analysis")
    def setup_notebook_environment(self):
        """Setup coordinated notebook environment"""
        notebook_config = {
            "kernel": "python3",
            "extensions": ["nbextensions", "jupyter_contrib_nbextensions"],
            "coordination_enabled": True,
            "auto_save": True
        }
        
        cf.memory.store("notebook_config", notebook_config)
        cf.hooks.notify("Notebook environment configured")
        
    @cf.during_task("cell_execution")
    def coordinate_cell_execution(self, cell_content, cell_type):
        """Coordinate notebook cell execution"""
        if cell_type == "code":
            # Store cell execution context
            cf.memory.store(f"cell_execution_{hash(cell_content)}", {
                "content": cell_content,
                "timestamp": "2024-01-01T00:00:00Z",
                "status": "executed"
            })
            
            cf.hooks.notify(f"Cell executed: {cell_content[:50]}...")
            
    @cf.post_task("notebook_complete")
    def finalize_notebook(self, notebook_path):
        """Finalize notebook and extract key findings"""
        findings = {
            "key_insights": [],
            "model_performance": {},
            "next_steps": [],
            "notebook_path": notebook_path
        }
        
        cf.memory.store("notebook_findings", findings)
        cf.hooks.notify(f"Notebook analysis complete: {notebook_path}")

🎯 SPECIALIZED ML WORKFLOWS

Computer Vision Pipeline:

# cv_pipeline_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class ComputerVisionCoordinator:
    
    @cf.pre_task("cv_pipeline_setup")
    def setup_cv_pipeline(self):
        """Setup computer vision pipeline"""
        cv_config = {
            "frameworks": ["torch", "torchvision", "opencv"],
            "data_augmentation": True,
            "pretrained_models": ["resnet50", "efficientnet"],
            "batch_size": 32,
            "image_size": 224
        }
        
        cf.memory.store("cv_config", cv_config)
        cf.hooks.notify("Computer vision pipeline configured")
        
    @cf.parallel_task("image_preprocessing")
    def preprocess_images(self, image_batch):
        """Preprocess images in parallel"""
        config = cf.memory.retrieve("cv_config")
        
        # Parallel image processing
        processed_images = []
        for image in image_batch:
            processed = self.apply_transforms(image, config)
            processed_images.append(processed)
            
        cf.memory.store("processed_batch", processed_images)
        cf.hooks.notify(f"Processed {len(processed_images)} images")

Natural Language Processing Pipeline:

# nlp_pipeline_coordinator.py
import claude_flow_hooks as cf

@cf.coordination_required
class NLPCoordinator:
    
    @cf.pre_task("nlp_pipeline_setup")
    def setup_nlp_pipeline(self):
        """Setup NLP processing pipeline"""
        nlp_config = {
            "frameworks": ["transformers", "spacy", "nltk"],
            "models": ["bert-base-uncased", "gpt-2"],
            "tokenization": "wordpiece",
            "max_length": 512,
            "batch_size": 16
        }
        
        cf.memory.store("nlp_config", nlp_config)
        cf.hooks.notify("NLP pipeline configured")
        
    @cf.parallel_task("text_preprocessing")
    def preprocess_text(self, text_batch):
        """Preprocess text data in parallel"""
        config = cf.memory.retrieve("nlp_config")
        
        # Parallel text processing
        processed_texts = []
        for text in text_batch:
            processed = self.apply_nlp_transforms(text, config)
            processed_texts.append(processed)
            
        cf.memory.store("processed_text_batch", processed_texts)
        cf.hooks.notify(f"Processed {len(processed_texts)} texts")

🚀 GETTING STARTED

Quick ML Project Setup:

# Initialize ML project with swarm coordination
npx claude-flow@alpha init --template ml_project --agents 8

# Or with specific ML focus
npx claude-flow@alpha init --template deep_learning --agents 6
npx claude-flow@alpha init --template computer_vision --agents 7
npx claude-flow@alpha init --template nlp_project --agents 8

Environment Setup:

# Install ML dependencies
pip install torch torchvision tensorflow scikit-learn pandas numpy
pip install mlflow wandb optuna great-expectations
pip install prometheus-client grafana-api

# Setup coordination hooks
claude-flow hooks install --ml-mode

📋 ML PROJECT CHECKLIST

Pre-Development:

Data availability and quality assessment
Privacy and compliance requirements
Model performance requirements
Deployment target identification
Resource requirements estimation

Development Phase:

Data pipeline implementation
Feature engineering automation
Model architecture selection
Hyperparameter optimization
Cross-validation implementation

Validation Phase:

Deployment Phase:

Post-Deployment:

Remember: Claude Flow coordinates ML workflows, Claude Code implements them. Use parallel execution for all ML pipeline operations to maximize efficiency and coordination effectiveness.

CLAUDE MD AI ML Projects

Claude Code Configuration for AI/ML Projects

🚨 CRITICAL: PARALLEL ML PIPELINE EXECUTION

🧠 AI/ML AGENT SPECIALIZATION

Required Agent Types for ML Projects:

🚀 ML SWARM INITIALIZATION PATTERN

Standard ML Project Setup:

📊 ML PIPELINE TODOWRITE PATTERN

ML-Specific Todo Categories:

🛠️ ML FRAMEWORK INTEGRATION

TensorFlow/Keras Projects:

PyTorch Projects:

🔄 ML WORKFLOW ORCHESTRATION

End-to-End ML Pipeline:

📈 MODEL TRAINING COORDINATION

Distributed Training Pattern:

🔍 MODEL EVALUATION & VALIDATION

Comprehensive Model Testing:

🚀 MLOPS DEPLOYMENT COORDINATION

Kubernetes Deployment Pipeline:

📊 DATA SCIENCE EXPERIMENT TRACKING

MLflow Integration:

🔐 ML SECURITY & PRIVACY

Privacy-Preserving ML Coordination:

📈 PERFORMANCE MONITORING

Real-time Model Performance Tracking:

🧪 A/B TESTING FRAMEWORK

Coordinated A/B Testing for ML Models:

🏗️ ML PROJECT STRUCTURE

Standard ML Project Template:

📚 JUPYTER NOTEBOOK COORDINATION

Coordinated Notebook Development:

🎯 SPECIALIZED ML WORKFLOWS

Computer Vision Pipeline:

Natural Language Processing Pipeline:

🚀 GETTING STARTED

Quick ML Project Setup:

Environment Setup:

📋 ML PROJECT CHECKLIST

Pre-Development:

Development Phase:

Validation Phase:

Deployment Phase:

Post-Deployment:

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