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If you’ve worked in AI and machine learning, you probably know that getting good training data is one of the core problems with building good models.
And in many cases, we need a lot of training data to build models that generalize well, meaning, models that perform well both during training, but also on new examples once the model is deployed....

Dealing with the problem of overfitting is one of the core issues in machine learning and AI.
Your model seems to work perfectly on the training set, but when you try to validate it on the test set … it’s terrible.
This is a core problem in machine learning, especially when you start using advanced ML techniques like deep learning....

If you want to build high-performing machine learning and AI systems, then simply training those systems is rarely enough.
You often need to build multiple models, often with multiple different algorithms, and then compare the different models to each other to see which is best....

In machine learning, making sure that you have a model that performs well is, in some sense, the most important thing.
This means that you need to be really good at evaluating different models.
But, this can be a challenge, as you run into issues like overfitting, hyperparameter optimization, and model selection....