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Building Reusable Pipeline Functions

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Overview

This course lays the groundwork for a robust MLOps pipeline by developing core functions that will be reused in subsequent courses. Rather than focusing on the full data science process, learners will implement specific, modular components for data processing, model training, evaluation, and persistence—all critical for later integration in automated retraining and API-based serving.

Syllabus

Unit 1: Building Reusable Data Processing Functions

Fix the Data Processing Bug
Loading Diamonds Dataset Correctly
Identifying Data Columns Efficiently
Building a Robust Preprocessing Pipeline
Building a Data Processing Module

Unit 2: Model Training and Prediction Functions in ML Pipelines

Setting Default Hyperparameters in Models
Linear Regression Model Training
Consistent Model Predictions
Enhance Prediction Function Reliability
Building a Complete ML Pipeline

Unit 3: Model Evaluation: Completing the Machine Learning Pipeline

Debugging Model Evaluation Metrics
Enhance Model Evaluation Function
Generate Predictions for Model Evaluation
Model Evaluation with Key Metrics
Building a Complete ML Pipeline

Unit 4: Model Persistence: Saving and Loading Machine Learning Models

Enhance Model Metadata Storage
Enhance Model Loading Resilience
Enhance Your Model Saving Skills
Saving Model Metadata to Disk
Loading Models with Precision
Model Persistence with Joblib and JSON

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