What are the differences between AutoML and traditional machine learning?

What are the differences between AutoML and traditional machine learning?

AutoML automates the process of applying machine learning, while traditional ML requires manual intervention for model selection and tuning.

Can you give me examples of when to use AutoML?

AutoML is great for quick prototyping, when you lack deep ML expertise, or when you need to handle large datasets efficiently.

What are the limitations of AutoML?

Some limitations include less control over the model, potential overfitting, and it may not perform well on very specialized tasks.

Is traditional ML still relevant?

Yes, traditional ML is still relevant, especially for complex problems where domain expertise and fine-tuning are crucial.

Which one is better for small businesses?

It depends on the business's needs. AutoML can be more accessible for small businesses without extensive ML resources.