Abstract: 

Instead of deploying their talents and knowledge, most data scientists today are still forced to slog through the data discovery and acquisition process, which can take anywhere from a few days to a few months.
What if you could cut that down to a few minutes instead? Data science automation has historically focused on hyperparameter tuning and model optimization but now it’s time to see how new tools can empower data scientists to use more and better data.

This technical track covers:
Why augmented data discovery is a major benefit, not a major drawback
How innovations in machine learning are focused on simplifying the data discovery and acquisition process
How augmenting data discovery enriches your machine learning models and gives you better results

Bio: 

Victor Ghadban has over 20 years of experience in AI and ML, starting at FICO where he was involved in Fraud Predictive Analytics for the credit financial sector and automated credit application workflow engines. then he moved onto Criminal Justice Analytics, after which he focused on Housing Analytics and most recently Victor was field CTO in AI/ML for Hewlett Packard Enterprise, and now he is the Head Field Data Scientist at Explorium, working on his passion and pursuit in evangelizing the importance of data enrichment and AI to help enable customers everywhere.