In this workshop, we will focus on one of the most important aspect of machine learning i.e., data preparation. We will also discuss how Automl could be used to automate data prep and will have a hands-on demo session with real-world datasets.
You will learn how to:
(i) do enterprise-scale data preparation.
(ii) perform ETL operations without writing extensive code.
(iii) configure custom neural networks to apply to real-world problems such as predictive modeling, forecasting, and personalization.
We will start with a technical talk on data munging for deep learning and then we will walk through 2 real-world use-cases - Churn Reduction and Lead Scoring to demonstrate data ingestion from multiple sources, data preparation required for training custom neural networks, and then finally conclude with evaluating the trained deep learning models.
Welcome and workshop overview
Overview on deep learning, data prep, automl core concepts
Hands on code lab
Q&A and wrap up
Head of Content & Developer Relations at abcuas.ai
CEO and Co-Founder of Abacus.AI. she was the General Manager for AI Verticals at AWS, AI. Her organization created and launched Amazon Personalize and Amazon Forecast, the first of their kind AI services that enable organizations to create custom deep-learning models easily. Prior to that, she was the CEO and co-founder of Post Intelligence that was acquired by Uber. Bindu was previously at Google