I spoke with Razi Raziuddin, CEO of FeatureByte, about the best way to prep data for ML models; he also explained some of the most common challenges with feature engineering.
Among the topics we discussed:
- The process of transforming raw data into features – to train ML models and predict future outcomes – is called feature engineering. Why is feature engineering so challenging for companies?
- What advice do you give companies about the best way to prep data for ML models?
- How is FeatureByte addressing the feature engineering needs of its clients?
- The future of data prep and ML models? Will it get easier?
Listen to the podcast:
Also available on Apple Podcasts
Watch the video: