What is AI Retraining?

What is AI Retraining?

AI Retraining is the practice of periodically or continuously updating an AI model using fresh data so that its predictions, classifications, or outputs remain accurate and relevant. When an AI model is first built, it learns patterns from a specific dataset that represents a point in time. As the world changes, those patterns become less representative of current reality, and the model's performance declines. Retraining corrects this by teaching the model new patterns from more recent data.

Consider a practical example. A demand forecasting model for a retail business was trained on two years of historical sales data. It performs well initially. But over the following months, consumer preferences shift, a new competitor enters the market, and seasonal patterns change. Without retraining, the model's forecasts become increasingly inaccurate, leading to inventory mismanagement and lost revenue.

Why AI Models Need Retraining

Data Drift

Data drift occurs when the statistical properties of the data the model encounters in production differ from the data it was trained on. There are two main types:

• Concept drift: The relationship between inputs and outputs changes. For example, what constituted a high-risk loan application two years ago may be different today due to economic changes.
• Feature drift: The distribution of input data changes. For example, customer demographics shift, new product categories are introduced, or business processes change.

Environmental Changes

External factors that affect AI performance include:

• Market conditions: Economic shifts, competitive changes, and industry trends
• Regulatory changes: New compliance requirements that alter what data is available or how decisions must be made
• Seasonal patterns: Changes in cyclical behaviour from year to year
• Black swan events: Unexpected events like pandemics or economic crises that fundamentally alter patterns

Feedback Incorporation

Retraining is also the mechanism for incorporating the corrections and feedback that human users provide when they override or adjust AI outputs. This human feedback is some of the most valuable training data available because it reflects real-world expert judgement.

Retraining Approaches

Scheduled Retraining

The simplest approach is retraining on a fixed schedule, such as weekly, monthly, or quarterly. This works well when:

• Data patterns change gradually and predictably
• The cost of retraining is moderate
• Slight performance degradation between retraining cycles is acceptable

Triggered Retraining

Retraining is triggered when performance monitoring detects that model accuracy has dropped below a defined threshold. This approach:

• Is more resource-efficient than fixed schedules because retraining happens only when needed
• Requires robust monitoring systems to detect performance degradation reliably
• Works well for models where performance changes are unpredictable

Continuous Learning

In continuous learning systems, the model updates incrementally as new data arrives rather than being retrained from scratch. This approach:

• Keeps the model perpetually current
• Is technically more complex to implement and monitor
• Requires careful safeguards to prevent the model from learning from bad data or temporary anomalies
• Is appropriate for high-frequency applications where timeliness of learning is critical

Full Retraining vs. Fine-Tuning

• Full retraining: Building a new model from scratch using the complete historical dataset plus new data. More thorough but more expensive and time-consuming.
• Fine-tuning: Updating the existing model with only new data. Faster and less resource-intensive but may not fully address major shifts in data patterns.

The choice depends on how significantly the data landscape has changed and the computational resources available.

The Retraining Process

1. Performance Monitoring

Continuous monitoring tracks model performance against key metrics. When performance crosses defined thresholds, retraining is triggered or flagged for review.

2. Data Preparation

New training data must be collected, cleaned, validated, and prepared. This is often the most time-consuming step and benefits significantly from strong AI Data Ops practices.

3. Model Training

The model is retrained using the updated dataset. This may involve the same model architecture or an updated architecture if the original approach is no longer optimal.

4. Validation and Testing

The retrained model is evaluated against test data and compared to the current production model. Key checks include:

• Overall accuracy improvement
• Performance across different segments and edge cases
• Absence of new biases or failure modes
• Consistency with business rules and constraints

5. Deployment

Once validated, the retrained model replaces the production model. This should be done through a controlled deployment process, often using canary or blue-green deployment strategies to minimise risk.

6. Post-Deployment Monitoring

After deployment, closely monitor the retrained model to confirm it performs as expected in production, not just in testing.

Retraining Considerations for Southeast Asian Businesses

• Diverse market dynamics: Models serving multiple ASEAN markets may need different retraining schedules for each market, as conditions change at different rates across countries.
• Data availability: In some markets, the volume of new data may be smaller, making frequent retraining more challenging. Consider transfer learning approaches that leverage data from higher-volume markets.
• Cost management: Retraining consumes computational resources and team time. For SMBs, balancing retraining frequency against cost is a practical concern. Cloud-based training can help manage costs through pay-per-use pricing.
• Seasonal patterns: ASEAN markets have region-specific seasonal patterns, from Ramadan to Chinese New Year to monsoon seasons, that models must learn to handle.

Common Retraining Mistakes

• Retraining too infrequently: Allowing model performance to degrade significantly before retraining, resulting in poor decisions and lost trust
• Retraining without validation: Deploying retrained models without thorough testing against the current production model
• Using poor quality new data: Retraining with data that has not been properly cleaned and validated can make the model worse, not better
• Ignoring historical context: Retraining only on recent data may cause the model to forget patterns that remain relevant, such as annual seasonal cycles
• No rollback plan: Failing to maintain the ability to revert to the previous model version if the retrained model performs poorly in production