From Notebook to Production

From Notebook to Production

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Machine Learning (ML) in the Banking, Financial Services, and Insurance (BFSI) sector is no longer a luxury; it’s a necessity. From credit scoring to fraud detection, ML models are at the heart of many automated decisions. However, most ML projects start humbly in Jupyter notebooks and often struggle to transition into robust, production-ready systems. In this blog post, we will take a comprehensive look at how to bridge that gap, using an end-to-end ML workflow and code architecture that scales.

Let’s break it down into two major sections:

  1. Understanding the ML Workflow
  2. Converting to a Production-Ready ML Codebase

To keep things practical and relatable, we’ll use a specific BFSI use case: Predicting Loan Default Risk.

📉 Use Case: Predicting Loan Default Risk

Let’s say you’re a data scientist at a bank. Your team is responsible for evaluating loan applications. Your task is to build a machine learning model that predicts whether an applicant is likely to default on a loan.

Problem Statement

Given a dataset of previous loan applications, build a model to predict whether a new applicant will default on their loan.

  • Input features: Age, salary, loan amount, credit history, employment status, etc.
  • Output variable: Binary label indicating default (Yes or No)

🔬 Step 1: The Machine Learning Process (Based on Image 1)

Here’s a detailed walkthrough of each step in the ML process as shown in the first image:

📊 1. Initial Dataset

The raw data is often collected from various internal systems (loan applications, customer profiles, transaction history) and third-party sources (credit scores, government databases).

Tasks:

  • Centralize data in a usable format (CSV, SQL, Parquet)
  • Remove duplicates
  • Identify data schema and types

🤝 2. Exploratory Data Analysis (EDA)

This step helps you understand the structure, distribution, and patterns in your dataset.

Tools/Methods:

  • PCA (Principal Component Analysis) for dimensionality reduction
  • SOM (Self-Organizing Maps) for visualization of high-dimensional clusters

You would typically do this in a Jupyter notebook, using pandas, seaborn, and matplotlib.

♻️ 3. Data Cleaning & Preprocessing

Transform the dataset into a usable form:

  • Handle missing values (imputation or removal)
  • Normalize or standardize numerical features
  • Encode categorical features using one-hot or label encoding
  • Validate that the data meets the i.i.d. assumption

✖️ 4. Data Splitting

Split your dataset:

  • Training set (80%) to build the model
  • Test set (20%) to evaluate the model’s performance

Use stratified splitting if the classes are imbalanced.

⚙️ 5. Model Selection and Training

Choose a suitable algorithm:

  • SVM (Support Vector Machine) for margin-based classification
  • KNN (K-Nearest Neighbors) for intuitive distance-based models
  • DL (Deep Learning) for more complex patterns

Tune hyperparameters using GridSearchCV, RandomizedSearchCV, or tools like Optuna.

🔄 6. Model Evaluation

Select metrics based on the problem type:

  • Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
  • Regression: MSE (Mean Squared Error), RMSE, MAE

Visualize with confusion matrices, ROC curves, and residual plots.

📊 7. Model Deployment Readiness

After training and evaluating the model, package it with:

  • Saved weights using joblib or pickle
  • Preprocessing pipeline using sklearn.pipeline
  • Validation artifacts

At this point, you have a working ML model in a Jupyter notebook. But it’s not yet ready for production.

🤖 Step 2: Converting to a Production-Ready ML Codebase (Based on Image 2)

Now, let’s organize our project like a software engineer. Here’s the ideal project structure:

ml-loan-default-predictor/
│
├── data/
│   ├── raw/               <- Unprocessed data files
│   ├── processed/         <- Cleaned and transformed data
│   └── external/          <- External or public datasets
│
├── notebooks/             <- Jupyter notebooks for prototyping
│
├── src/                   <- All source code
│   ├── data/
│   │   ├── load_data.py        <- Load data from disk or database
│   │   └── preprocess.py       <- Data cleaning, normalization, encoding
│   │
│   ├── features/
│   │   └── build_features.py   <- Domain-specific feature engineering
│   │
│   ├── models/
│   │   ├── train_model.py      <- Train classifier
│   │   └── evaluate_model.py   <- Metrics, visualization, logs
│   │
│   ├── visualization/
│   │   └── visualize.py        <- Confusion matrices, feature importance
│   │
│   └── utils/
│       └── helper_functions.py <- Logging, config management
│
├── tests/                 <- Unit tests using pytest or unittest
├── .gitignore             <- Files to ignore in version control
├── README.md              <- Project description and instructions
├── requirements.txt       <- Dependency list
└── main.py                <- Script to orchestrate the pipeline

🚀 From Research to Production: The Flow

1. Prototype in notebooks/

Use Jupyter notebooks to explore, visualize, and validate hypotheses. Save plots, charts, and early insights.

2. Modularize into src/

Move code into dedicated scripts:

  • load_data.py fetches raw CSVs or connects to databases
  • preprocess.py includes all cleaning logic used in your notebook
  • build_features.py encodes domain logic (e.g., “loan-to-income ratio”)

3. Model Training

Wrap your training logic in train_model.py. Log model artifacts, scores, and hyperparameters.

4. Evaluation

Move all visualizations and metric calculations into evaluate_model.py. Create artifacts for dashboards or internal review.

5. Run End-to-End

Run the pipeline via main.py. This can be automated using cron jobs, Airflow DAGs, or CI/CD pipelines.

🌟 Bonus Engineering Tips

  • Use MLflow to track experiments, parameters, and results.
  • Use Docker to containerize your training environment.
  • Integrate with FastAPI or Flask for REST APIs to serve predictions.
  • Deploy models using AWS SageMaker, GCP AI Platform, or Azure ML.
  • Automate retraining pipelines with Apache Airflow or Kubeflow Pipelines.

📄 Conclusion

Transitioning from Jupyter notebooks to a scalable ML system requires more than just code; it demands structure, discipline, and software engineering principles. In the BFSI sector where accuracy, reproducibility, and auditability are critical, having a modular, testable, and scalable ML pipeline is non-negotiable.

Cheers,

Sim