Data-centric AI (DCAI): Difference between revisions

Data-centric AI (DCAI) (view source)

303 bytes added , 2 March 2023

57

edits

@@ Line 35: / Line 35: @@
 ==Reasons for Data-centric AI==
-*Data quality issues alone are estimated to cost the United States $3 Trillion annually.
+In the US, data quality problems cost $3 trillion per year. It is difficult to guarantee data quality in large datasets without using algorithms. ChatGPT, a ML system that relies on human feedback to correct shortcomings arising out of low-quality training data has used ChatGPT as an example. However, automated methods are required to ensure that ML models are trained using clean data. Recent research has highlighted the importance of data-centric AI. This is an approach that uses simple methods to change the dataset and creates more accurate models. This course will teach you how to improve any ML model using its data. It can be used to train and supervised ML models.
-*Automated methods, systematic engineering principles and automated methods are required to ensure that ML models are trained using clean data.
-*Recent research has shown that simple methods that adapt to changing data can produce more accurate models than complex modeling strategies.