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Big Data [clear filter]
Saturday, October 20


Flexible and Fast Storage with Alluxio for Deep Learning
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.

avatar for Bin Fan

Bin Fan

Founding Member & Architect, Alluxio
Bin Fan is the founding member of Alluxio Inc and the PMC member of Alluxio open source project. Prior to Alluxio, he worked for Google to build the next-generation storage infrastructure and won Google’s Technical Infrastructure award. Bin got his Ph.D. in Computer Science from... Read More →

Saturday October 20, 2018 12:30pm - 1:00pm
Ballroom # 408A


Demand Forecasting with Real World Data
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 .
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

avatar for Sarat Tatineni

Sarat Tatineni

Senior Manager, Cognizant
I am a development manager , currently leading a team of data analysts for an Insurance client. My areas of expertise include predictive analytics, modeling, business intelligence , big data and data warehousing. I work on platforms & languages such as Python , Scala, Spark ML , SQL... Read More →

Saturday October 20, 2018 3:00pm - 3:30pm
Ballroom # 408A