Data Science Workflow
Montana emphasizes the importance of data curation in data science work, highlighting the significance of feature engineering and data cleaning. Postgres offers robust capabilities for manipulating data, allowing for efficient model building and deployment. The discussion delves into creating training data tables or views and the nuances of feature engineering in data science workflows.In this clip
From this podcast
Practical AI
Machine learning in your database
Related Questions
What is the process of training a machine learning model as discussed in the episode Machine learning in your database and the clip ML in Postgres?
What are some techniques for training machine learning models as discussed in the episode Machine learning in your database and the clip ML in Postgres?
What is the process of training a machine learning model as discussed in the episode Machine learning in your database and the clip Postgres ML Dashboard?