$ show paiml/practical-mlops-book
This repository serves as the companion code for the O'Reilly book 'Practical MLOps', providing a collection of examples spanning the MLOps lifecycle. It is a hybrid repository with a dominant data research character, containing Jupyter notebooks for exploratory data analysis (e.g., COVID-19 trends, sugar intake correlations), AutoML experiments (Ludwig text classification), and model interoperability scripts (ONNX conversion). It also includes application-like components such as a Flask-based model serving container, CLI tools for CSV linting, and cloud deployment scripts for AWS, Azure, and GCP. The code is organized by book chapters and demonstrates practical MLOps patterns, though many examples are simplified for educational purposes rather than production readiness. The most reusable asset is the modularized CSV linter, which provides a well-structured CLI for data quality checks.
The LLM identified 3 files worth highlighting for reuse:
Provides reusable data quality check functions (carriage_returns, unnamed_columns, zero_count_columns) that can be extracted and applied to any CSV dataset. The functions are isolated, well-named, and handle edge cases (TypeError). This module demonstrates a clean separation of concerns and is easily portable to other projects.
A command-line interface built with Click that orchestrates the data quality checks from checks.py. It accepts a CSV file path, runs multiple validations, and outputs warnings. The pattern of separating CLI logic from business logic makes it reusable and testable. Could be adapted to other data validation tasks.
A simple CLI tool that loads an ONNX model, validates it using onnx.checker.check_model, and prints a human-readable graph. This is a reusable utility for any ONNX model verification, demonstrating a clean pattern for model interoperability tasks.