In my opinion, based on my experience: 1. Monitor data drift: Regularly check for shifts between training and production data using statistical tools and expert insights. Balance performance with retraining costs. 2. Address core issues: If model lags without data drift, review feature engineering, assess over fitting, or reconsider model choice. Use DAGs to clarify variable relationships. 3. Stay current: Keep up with industry standards and research. Implement valuable improvements when possible. 4. Evaluate model value: Regularly assess cost-benefit ratios. Don't hesitate to switch to simpler, cost-effective alternatives if a complex model under performs.

To keep my machine learning models competitive, I focus on: 1. Regular Data Updates: Continuously refining and expanding datasets to maintain relevance and accuracy. 2. Algorithm Optimization: Exploring and implementing new algorithms or techniques for better performance. 3. Frequent Benchmarking: Comparing model performance against industry standards to identify gaps. 4. Collaboration: Working with experts and gathering feedback for ongoing improvements. 5. Staying Current: Keeping up with the latest research and trends in machine learning and AI.

If your competition is outperforming your ML model, it could be due to the usual suspects. Model drift i.e. model degradation or data drift i.e. distribution shift. If your business performance is dependent on the ML model, data drift can also occur due to a superior model from the competition. Some strategies I would deploy are: 1. Continue to monitor drift and refresh data and retrain model to bring performance back to par. 2. If you have the luxury of a large ML team, hold a kaggle type competition to build a more accurate ML model. 3. Augment training data with external data sets. Chances are that the competition now has more information you need to compete with. 4. Assess data quality for any degradation.

Easiest hack on lighter note are: 1.Just reverse engineer your competition model , ask right questions to competitor models and derive insights 2. Hire right talent from your competitors

If you work in an area with strong competition and the necessity to have the best model, then it will not be enough to just look at the obvious tasks of keeping data and models up to date. This will only lead to incremental improvements that may or may not keep you in the competition. Instead, widen the scope and think about challenges that your current machine learning model cannot tackle at all, neither now nor with incremental changes in the future. Observe the research on fundamental machine learning methods development and be creative in applying those methods to solve problems about which your competitors (and colleagues) say 'this cannot be done'.