Abstract: 

Predicting the future has always been a fascinating topic. Now we have AI tools and techniques that can help us do it better than ever before. In this session, we'll cover the fundamentals of solving time-series problems with AI, and show how it can be done with popular data science tools such as Pandas, TensorFlow, and the Google Cloud AI Platform.

We'll start with how to visualize, transform, and split time-series data for use in an ML model. We'll also discuss both statistical and machine learning techniques for predictive analytics. Finally, we'll show how to train a demand forecasting model in the cloud and make predictions with it. Attendees can access Jupyter notebooks after the session to review the material in more detail.

Bio: 

Karl Weinmeister is a Developer Relations Engineering Manager at Google, based out of Austin, Texas. Karl leads a global team of data science and ML engineering experts in the Developer Advocacy organization, who build technical assets and consult with enterprise customers on Artificial Intelligence and Machine Learning. Karl was a contributor to Proverb, an AI-based crossword puzzle solver, which competed at the American Crossword Puzzle Tournament.