10 different ways to improve a Machine Learning Model

After building a successful machine learning model and deploying to production. Maybe after few months you gathered a new set of training data and you want to improve the metrics of your model. In this post, I have mentioned 10 different ways on how you can try different ones to see which does a better job for you.

1. Collect more training data
2. Try collect more diverse training data that closely represents the real world data
3. Try running optimisation algorithms longer — gradient descent.
4. Try different optimisation algorithm — adam optimisation instead of gradient descent
5. Try building bigger network
6. Try shrinking the network
7. Try dropout
8. Add L2 regularization
9. Try changing network architectures like — activation function, number of hidden units etc.
10. Compare the test result with production result and figure out the potential pitfalls.

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