In real-world machine learning (ML) applications, models need to be continuously updated with new data to maintain high accuracy and relevance. Static models degrade over time as new patterns emerge in data. Instead of retraining models from scratch, incremental learning (online learning) enables models to update using only new data, making the process more efficient.

This tech concept will detail how to create an ML pipeline that automates incremental training with new data and deploys updated models for prediction. In my ~20-year tech career, I’ve been a catalyst for innovation, architecting scalable solutions that lead organisations to extraordinary achievements. My trusted advice inspires businesses to take bold steps and conquer the future of technology.  

Why Incremental Learning?

Incremental learning has several advantages:

• Efficient Updates: Eliminates the need to retrain from scratch by learning from new data.
• Reduced Memory Usage: Does not require storing and processing the full dataset.
• Handles Streaming Data: Adapts to real-time or continuously incoming data.
• Scalability: Suitable for large-scale applications with evolving datasets.

Models Supporting Incremental Learning in Scikit-Learn

Not all models in scikit-learn support incremental learning. The following models can be trained using partial_fit():

Linear Models

• SGDClassifier
• SGDRegressor
• PassiveAggressiveClassifier
• PassiveAggressiveRegressor

Naïve Bayes Models

• GaussianNB
• MultinomialNB
• BernoulliNB

Neural Networks

• MLPClassifier
• MLPRegressor

Decomposition Techniques

• IncrementalPCA
• MiniBatchDictionaryLearning

Clustering Models

• MiniBatchKMeans

For deep learning models (TensorFlow/Keras, PyTorch), incremental learning can be implemented using saved weights and fine-tuning.

Sample Data Format

Before diving into the pipeline, let’s define the sample data format used for training and predictions.

Historical and New Data Format (CSV or Database Table)

ID,Feature1,Feature2,Feature3,TARGET
1,10.2,15.6,100,1
2,12.4,16.1,110,0
3,11.8,14.9,105,1

• Feature1, Feature2, Feature3: Independent variables (input features).
• TARGET: Dependent variable (label for supervised learning).

ML Pipeline with Incremental Learning

The pipeline consists of multiple steps:

1. Data Ingestion: Collecting new data from sources (databases, APIs, streams, logs, etc.).
2. Data Preprocessing: Cleaning and transforming the new data.
3. Incremental Model Training: Updating the model using only new data.
4. Model Versioning & Storage: Saving models systematically for tracking.
5. Deployment & Serving: Updating the deployed model with the latest version.

Step 1: Define the Pipeline Steps

from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler
import pandas as pd
from sqlalchemy import create_engine
import joblib
import os
import numpy as np

class IncrementalTrainingPipeline:
    def __init__(self):
        self.scaler = StandardScaler()
        self.model = SGDClassifier(loss='log')  # Incremental learning classifier
    
    def load_new_data(self):
        # Load data from multiple sources (SQL, CSV, API)
        engine = create_engine('sqlite:///new_data.db')
        db_data = pd.read_sql('SELECT * FROM new_records', engine)
        
        csv_data = pd.read_csv('new_data.csv') if os.path.exists('new_data.csv') else pd.DataFrame()
        
        api_data = pd.DataFrame()  # Placeholder for API data fetch logic
        
        # Combine all sources
        return pd.concat([db_data, csv_data, api_data], ignore_index=True)
    
    def preprocess_data(self, data):
        X = data.drop(columns=['TARGET'])
        y = data['TARGET']
        return X, y
    
    def incremental_train(self, new_data):
        X_new, y_new = self.preprocess_data(new_data)
        
        if os.path.exists('scaler.pkl'):
            self.scaler = joblib.load('scaler.pkl')
            X_new = self.scaler.transform(X_new)
        else:
            X_new = self.scaler.fit_transform(X_new)
            joblib.dump(self.scaler, 'scaler.pkl')
        
        if os.path.exists('latest_model.pkl'):
            self.model = joblib.load('latest_model.pkl')
        
        self.model.partial_fit(X_new, y_new, classes=np.array([0,1]))
        joblib.dump(self.model, 'latest_model.pkl')

# Initialize and run pipeline
pipeline = IncrementalTrainingPipeline()
new_data = pipeline.load_new_data()
pipeline.incremental_train(new_data)

Step 2: Model Deployment & API for Predictions

from flask import Flask, request, jsonify
import numpy as np
import joblib

app = Flask(__name__)

def load_latest_model():
    return joblib.load('latest_model.pkl')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    input_data = np.array(data['features']).reshape(1, -1)
    model = load_latest_model()
    prediction = model.predict(input_data)
    return jsonify({'prediction': prediction.tolist()})

if __name__ == '__main__':
    app.run(debug=True)

Sample Input for API Prediction (JSON Format)

{
    "features": [11.5, 15.3, 108]
}

Step 3: Automating with Cron or Airflow

Schedule the incremental training process automatically:

0 2 * * * python incremental_train.py

This runs incremental_train.py every day at 2 AM.

My Tech Advice: Incremental learning offers an efficient approach to training ML models by updating only with new data instead of retraining on the entire dataset. Utilizing methods like partial_fit() in SGDClassifier, the model evolves continuously while minimizing computational costs. By adopting this approach, businesses can stay ahead of changing trends, make data-driven decisions, and maintain up-to-date ML models in a cost-effective manner.

#AskDushyant

Note: The example and pseudo code is for illustration only. You must modify and experiment with the concept to meet your specific needs.

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