Building a CI/CD Pipeline for Machine Learning: A Step-by-Step Guide

CI/CD Pipeline for a Machine Learning Project

From raw data to a production-ready model — fully automated

Continuous Integration and Continuous Deployment (CI/CD) ensures that machine learning code, data, and models are always in a deployable state. By automating data processing, training, and model logging, teams can reduce errors, improve reproducibility, and ship models faster.

Pipeline Overview

This pipeline is structured into clearly defined stages. Each stage performs a single responsibility and passes its output to the next stage, ensuring traceability and automation.

Stage-by-Stage Breakdown

📥 Stage 1: Load Data

This stage loads the raw dataset required for training and evaluation.

• Command: Executes stage2_load_data.py with params.yaml
• Dependencies:
  • stage1_Get_Data.py
  • stage2_Load_Data.py
  • Consignment_pricing_raw.csv
• Output: Raw data stored at data/raw/Consignment_pricing_raw.csv

python stage2_load_data.py --config params.yaml

Objective: Fetch and store raw data for downstream processing.

🧹 Stage 2: Preprocessing

Raw data is cleaned and transformed into a usable format.

• Command: stage3_preprocessing.py
• Dependencies: Raw dataset and preprocessing script
• Output: Consignment_pricing_processed.csv

python stage3_preprocessing.py --config params.yaml

Objective: Prepare clean, consistent data for feature engineering.

🧠 Stage 3: Feature Engineering

This stage enhances the dataset by creating or transforming features.

• Command: stage4_feature_engineering.py
• Dependencies: Processed dataset
• Output: data/transformed_data/Consignment_pricing_transformed.csv

python stage4_feature_engineering.py --config params.yaml

Objective: Improve model performance through better features.

✂️ Stage 4: Split Data

The dataset is split into training and test sets.

• Command: stage5_split_data.py
• Outputs:
  • Training data
  • Test data

python stage5_split_data.py --config params.yaml

Objective: Enable fair training and evaluation.

🤖 Stage 5: Train & Evaluate Model

The machine learning model is trained and evaluated.

• Command: stage6_train_evaluate.py
• Dependencies: Train/Test data + params.yaml
• Model: RandomForestRegressor (config-driven)

python stage6_train_evaluate.py --config params.yaml

Objective: Train a reproducible, evaluatable model.

📦 Stage 6: Log Production Model

The final model is logged into a model registry or tracking system.

• Command: log_production_model.py
• Purpose: Store model metadata and artifacts

python log_production_model.py

Objective: Track and manage production-ready models.

Why This CI/CD Pipeline Matters

This pipeline ensures that every data or code change automatically triggers retraining, evaluation, and logging.

💡 Key Takeaways

• Each pipeline stage has a single responsibility
• Automation ensures consistency and reproducibility
• Config-driven design simplifies experimentation
• Models are always production-ready
• Scales well with cloud platforms like AWS or GCP

CI/CD for machine learning — from experimentation to production