How to train AI ML models? Full pipeline in 15 mins.

Overview

The video presents a general workflow for building production-ready machine-learning models, particularly for beginners. The presenter emphasizes that the workflow should be adapted to the dataset rather than followed rigidly: mistakes at any stage can undermine the final model.

Machine-Learning Workflow

1. Clean the data. Handle missing values, corrupted or invalid samples, and duplicates. Duplicates can bias a model toward repeated examples, while invalid data should be assessed in context rather than removed automatically.

2. Transform data into a usable representation. Convert inputs such as images, audio, or text into numerical arrays or other forms the model can use. Consider reducing a large number of features, for example with PCA, and address class imbalance, potentially by augmenting underrepresented data. In language models, words or characters are encoded as numbers, and model outputs are decoded.

3. Preprocess features. Scale or normalize features so differences in their ranges do not make optimization unnecessarily difficult. The presenter mentions scikit-learn tools such as StandardScaler, MinMaxScaler, and Normalizer.

4. Split the dataset carefully. Common train/test split ratios include 80/20 and 70/30. Shuffle the data, and use stratification for classification when class proportions need to be preserved. Try multiple random splits, since performance on a single split may not represent real-world performance.

Fit preprocessing transformations on the training data, then apply the same fitted transformations to test and future data. Save preprocessing parameters along with the model.

1. Tune hyperparameters. Use a validation set to compare settings, then retrain with the selected settings on the training data before evaluating on the held-out test set. Examples include a random forest’s maximum depth and number of leaves.

2. Choose suitable models. Compare models appropriate to the data rather than defaulting to deep learning. Options mentioned include SVMs, decision trees, random forests, and linear regression. Consider both training time and prediction latency.

3. Evaluate performance. Check for overfitting—strong training results but poor performance on unseen data—as well as underfitting. Choose metrics suited to the task and dataset: accuracy can be misleading for imbalanced data, while F1 score may be more informative.

To assess uncertainty, evaluate across multiple random splits and report an average metric and confidence interval.

1. Account for real-world changes and deployment. Consider whether data changes over time. The presenter uses house prices and stock prices to illustrate how patterns learned from historical data may not hold later. Monitor for data or model drift, preserve preprocessing steps, and assess prediction speed where latency matters, such as on a fast manufacturing line.

Key Takeaway

This end-to-end introductory guide covers the ML training pipeline, from data preparation through model selection, evaluation, and real-world use. It highlights common pitfalls, including duplicates, class imbalance, inconsistent preprocessing, reliance on a single split, inappropriate metrics, and changing data.

Speaker and Source

The video features a single presenter from the ChemCoder channel.