What are some common mistakes in AI projects?

What are some common mistakes in AI projects?

Some common mistakes include not defining clear objectives, poor data quality, and lack of stakeholder involvement.

Can you elaborate on how poor data quality affects AI outcomes?

Poor data quality can lead to inaccurate models, biased results, and ultimately, failure to meet project goals.

What can be done to ensure data quality?

To ensure data quality, implement data validation processes, conduct regular audits, and involve domain experts in data collection.

Are there any specific tools you recommend for data validation?

Yes, tools like Great Expectations, DataRobot, and Talend can help with data validation and quality assurance.