"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

May 14, 2017

Kaggle Vs Enterprise Machine Learning Adoption - Two sides of coin

Reposting Summary from Quora Answer with my perspective added

What you don't learn in Kaggle Competitions
  • Determining business problem to solve with data
  • Real world data imbalance, Accuracy issues, Maintaining Models
  • Miss the challenges of data engineering (What features to select, causational vs correlation in domain context) 
What you learn by experimenting real world data science applications in Production
  • Identifying / Reusing Existing data for first level models 
  • Identifying pipelines to build for more relevant variables
  • ETL / Data Consolidation / Aggregation, Eliminating outliers / Redundant Data
Today's systems have enough Transactional Reporting  / BI Reports in place. The challenge is evolving from the current system, leveraging current data, build a basic model, slowly build pipelines and extend other machine learning use cases.

Happy Learning!!!

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