Fighting the Fade

Fighting the Fade: Keeping Your AI Models Sharp

Enterprise AI is a complex endeavor with several Blockers (or Rocks) impeding progress. Here’s one blocker and how to deal with it.

Don’t let model drift erode your AI investment – proactive monitoring and retraining are key.

The “Blocker”: Model Drift Issues

Imagine a finely tuned race car that gradually loses its edge over time. That’s what model drift is like for AI. It happens when the accuracy and performance of a deployed AI model degrade over time due to changes in the real-world data it encounters. This “drift” can be caused by shifts in customer behavior, new market trends, or even seasonal variations. The result? Inaccurate predictions, flawed decisions, and diminished ROI on your AI initiatives.

How to Overcome the Challenge:

• Continuous Monitoring is Key: Implement systems that continuously track key performance indicators (KPIs) of your AI models in production. Look for telltale signs of drift, such as declining accuracy, increasing error rates, or unexpected outputs.
• Embrace Champion-Challenger Testing: Deploy a “challenger” model alongside your existing “champion” model. This allows you to compare their performance in real-time and identify when the champion is starting to falter.
• Establish a Retraining Schedule: Don’t wait for drift to become a problem. Proactively retrain your models on fresh, updated data at regular intervals. This could be monthly, quarterly, or even more frequently depending on the volatility of your data.
• Version Control Your Models: Just like software, maintain different versions of your AI models. This allows you to roll back to a previous version if a new model performs poorly or exhibits unexpected behavior.
• Invest in Data Quality: Ensure the data used for retraining is of high quality, representative of the current environment, and free of biases. Data drift, where the input data changes over time, can significantly contribute to model drift.
• Consider Automated Retraining: Explore tools and platforms that automate the retraining process based on predefined triggers or performance thresholds. This can save time and ensure your models are always up-to-date.

Remember:

• Model drift is a natural phenomenon in AI, but it can be effectively managed with proactive monitoring, retraining, and data quality control.
• Addressing model drift ensures the long-term accuracy, reliability, and value of your AI investments.

Take Action:

• Assess your current AI models: Identify which models are most critical to your business and prioritize them for monitoring and retraining.

• Define performance thresholds: Establish clear metrics and thresholds that will trigger alerts or automated retraining.
• Explore monitoring and retraining tools: Research and evaluate different solutions available in the market that align with your needs and technical capabilities.
• Develop a model maintenance plan: Create a documented plan that outlines the monitoring, retraining, and version control processes for your AI models.

If you wish to learn more about all the Enterprise AI Blockers and How to Overcome the Challenges, visit: https://www.kognition.info/enterprise-ai-blockers