system-prompts-and-models-of-ai-tools/claude_skills/ml-model-integration/SKILL.md

name	description	allowed-tools
ml-model-integration	Expert in integrating AI/ML models into applications including model serving, API design, inference optimization, and monitoring. Use when deploying ML models, building AI features, or optimizing model performance in production.	Read, Write, Edit, Grep, Glob, Bash

ML Model Integration Expert

Purpose

Deploy and integrate machine learning models into production applications.

Capabilities

• Model serving (FastAPI, TensorFlow Serving)
• Inference optimization
• A/B testing models
• Model versioning
• Monitoring and drift detection
• Batch and real-time inference
• Feature stores

FastAPI Model Serving

from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np

app = FastAPI()

# Load model at startup
model = joblib.load('model.pkl')

class PredictionRequest(BaseModel):
    features: list[float]

class PredictionResponse(BaseModel):
    prediction: float
    confidence: float

@app.post('/predict', response_model=PredictionResponse)
async def predict(request: PredictionRequest):
    features = np.array([request.features])
    prediction = model.predict(features)[0]
    confidence = model.predict_proba(features).max()
    
    return PredictionResponse(
        prediction=float(prediction),
        confidence=float(confidence)
    )

@app.get('/health')
async def health():
    return {'status': 'healthy', 'model_version': '1.0.0'}

Model Monitoring

import mlflow

# Log model performance
with mlflow.start_run():
    mlflow.log_metric('accuracy', accuracy)
    mlflow.log_metric('precision', precision)
    mlflow.log_metric('recall', recall)
    mlflow.log_param('model_type', 'random_forest')
    mlflow.sklearn.log_model(model, 'model')

# Monitor drift
from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=train_data, current_data=prod_data)
report.save_html('drift_report.html')

Success Criteria

• ✓ Inference latency < 100ms
• ✓ Model accuracy monitored
• ✓ A/B testing framework
• ✓ Rollback capability
• ✓ Feature drift detected