AI Model Training: What Is Involved In A Single Training Cycle?

1. Data Input & Pre-Processing

Before training begins, the model is fed massive amounts of structured and unstructured data to ensure high-quality learning.
✅ Data cleaning & normalization – Removing duplicates, formatting inconsistencies, and biased inputs.
✅ Feature selection & embedding generation – Converting raw text, images, and numerical values into machine-readable formats.
✅ Tokenization & batching – Splitting text into smaller components and organizing data for efficient model processing.

2. Forward Propagation

(Inference Stage of Training)

The model processes input data through multiple layers of neurons, generating predictions.
✅ Each input flows through the network and is processed using learned weights.
✅ The model produces an output (e.g., predicted text, classification, or response).

3. Loss Calculation & Backpropagation

Once an output is generated, the model compares it to the expected answer and calculates its error.
✅ Backpropagation – The error is sent backward to adjust the model’s internal weights and biases.
✅ Gradient Descent Optimization – The model makes small adjustments to improve accuracy.

4. Parameter Updates & Model Refinement

As adjustments are made, the model re-runs the cycle with updated weight values.

✅ Each iteration improves the model’s ability to predict outputs more accurately.

5. Evaluation & Performance Monitoring

Once trained, the model undergoes validation testing on unseen data.

✅ Key performance metrics (accuracy, loss reduction, bias mitigation) are assessed.