Overview

Welcome to the aiqclib tutorial! This library provides a robust and streamlined framework for building and deploying machine learning models, specifically designed for applications that involve quality control of delayed-mode data and similar predictive tasks. It simplifies complex machine learning operations (MLOps) workflows by breaking them down into a clear, configurable three-stage process.

Basic Usage: The Three-Stage Workflow

aiqclib leverages powerful YAML configuration files to define every aspect of your machine learning pipeline, enabling reproducibility and easy experimentation. Once these configuration files are set up, executing a complete end-to-end workflow is straightforward:

Dataset Preparation: This initial stage transforms your raw input data into a clean, feature-engineered, and properly split dataset (training, validation, test sets) ready for model building.
```
import aiqclib as aq

prepare_config = aq.read_config("/path/to/prepare_config.yaml")
aq.create_training_dataset(prepare_config)
```
Training & Evaluation: In this stage, a machine learning model is trained and rigorously evaluated using the prepared dataset. This typically includes cross-validation and hyperparameter tuning to find the best performing model.
```
training_config = aq.read_config("/path/to/training_config.yaml")
aq.train_and_evaluate(training_config)
```
Classification (Inference): The final stage applies your trained model to new, unseen data to generate predictions or classifications. This is where your model goes from development to practical application.
```
classification_config = aq.read_config("/path/to/classification_config.yaml")
aq.classify_dataset(classification_config)
```

Objectives

You will learn how to run all three stages of aiqclib by creating stage-specific configuration files. This tutorial lets you create three classifiers for temp (temperature), psal (salinity), and pres (pressure) to predict QC labels for the corresponding variables.

Next Steps

Ready to get started? The next pages will guide you through the initial setup and then dive into the details of configuring each stage of your machine learning workflow with aiqclib.

Proceed to the next tutorial: Step 1: Installation.