What are the main challenges of automated machine learning?

What are the main challenges of automated machine learning?

Some key challenges include data quality, model interpretability, and the need for domain expertise.

How does data quality affect automated ML?

Data quality is crucial; poor data can lead to inaccurate models and unreliable predictions.

What about model interpretability?

Model interpretability is important for trust and understanding; complex models can be hard to explain.

Can automated ML work without domain expertise?

While automated ML can simplify processes, domain expertise is often needed to guide model selection and evaluation.

What solutions exist to overcome these challenges?

Solutions include better data management practices, using interpretable models, and involving domain experts in the process.