Unleashing an End-to-End Predictive Model Pipeline: A Step-by-Step Guide

A Detailed ML Ops Pipeline for an End-to-End Predictive Model for Tabular Data

This post provides a comprehensive guide on building an end-to-end predictive model pipeline for tabular data using XGBoost. The step-by-step implementation includes all essential stages of the ML Ops pipeline, such as data preparation, feature engineering, hyperparameter tuning, model explainability, and model monitoring. With the help of code snippets, you can easily follow along and implement this pipeline in your own projects. By the end of this guide, you will have a solid understanding of how to build a robust predictive model pipeline using XGBoost for tabular data.

Outlined below are the high-level steps involved in building an end-to-end predictive ML model:

1. Data preparation: Organizing the data in a suitable format for analysis and modeling. This includes sorting the data by timestamp, handling missing values, outliers and identifying any seasonal patterns or trends
2. Data analysis and visualization: Exploring the data to understand the trends and patterns for the predictors and target variables. This includes visualizing the data, calculating descriptive statistics, and identifying any correlations or dependencies between variables.
3. Feature engineering: Focusing on creating informative and relevant features by selecting only the relevant variables in…