Question 1

How much data do I need for machine learning?

Accepted Answer

It depends on the problem complexity. Simple models may work with thousands of records, while deep learning typically requires hundreds of thousands or millions. We can also use techniques like transfer learning to work with smaller datasets.

Question 2

How accurate are machine learning predictions?

Accepted Answer

Accuracy varies by problem and data quality. We set realistic expectations upfront and use proper validation techniques. Most production models achieve 80-95% accuracy depending on the use case.

Question 3

Can you explain how the model makes decisions?

Accepted Answer

Yes, we prioritize model interpretability. We use explainable AI techniques like SHAP values, feature importance, and attention visualization to understand and explain model decisions.

Question 4

How do you handle model updates and retraining?

Accepted Answer

We implement MLOps pipelines that automatically monitor model performance, detect drift, and trigger retraining when needed. This ensures your models stay accurate over time.

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