Machine Learning Integration: From Theory to Production

Introduction

Integrating machine learning into production systems requires careful consideration of data pipelines, model deployment, and monitoring.

ML Production Pipeline

// ML production pipeline
class MLProductionPipeline {
  async trainModel(trainingData: Dataset): Promise {
    const model = await this.mlFramework.train(trainingData);
    const evaluation = await this.evaluateModel(model);
    
    if (evaluation.accuracy > this.threshold) {
      await this.deployModel(model);
      return model;
    }
    
    throw new Error('Model accuracy below threshold');
  }
}
            

Bibliography

• Geron, A. (2019). "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"
• Burkov, A. (2019). "The Hundred-Page Machine Learning Book"