IDEAS SoCal AI & Data Science Conference 2018

In the age of growing data and increased computing power, deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI but also exposes some challenges for the access to data in different storage systems especially in cloud object storage. In this talk, we will describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.


Alluxio is an open-source memory-speed storage system, which unifies disparate persistent storage systems and provides data access as a local folder to the deep learning frameworks. With Alluxio, data scientists can gain easy access to a variety of storage systems (including Azure block store, AWS S3, and many others) and flexibility without the compromise on performance. This talk will use Tensorflow as an example to show how Alluxio can help data access and management for deep learning frameworks.

7 years ago , a consulting assignment for demand forecasting for one of our client sent us on a wild goose chase and brought some interesting actionable insights for our client. I am planning to do a working session with real world like data to derive insights while guiding the attendees on common pitfalls. We had done our initial analysis in SPSS, but I am planning to do the session on R/Python .
Agenda:
1) Introduction to Demand Forecasting Concepts - 5 mins
2) Algorithms typically used for Demand forecasting - 5mins
3) Introduce the data , business context - 5 mins
4) Tasks include cleaning data for outliers, null values, modelling - 30 mins
5) Analysis of Results and model selection - 5 mins
6) Analysis of Residual Error - 5 mins
7) Other Insights - 5 mins