You are viewing content from a past/completed QCon

Presentation: pDB: Abstraction for Modeling Predictive Machine Learning Problems

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 4:00pm - 4:10pm

Day of week: Tuesday

Share this on:

Abstract

In this talk, we will do a brief overview of modeling machine learning problems using Celect’s pDB framework. This forms the basis of the enterprise grade prediction analytics platform for retail and federal intelligence that we will describe in the longer talk. We will demonstrate how disparate predictive problems can be expressed using a common pDB language.

Speaker: Balaji Rengarajan

Senior Data Scientist @Celect

Balaji Rengarajan is responsible for architecting and engineering key aspects of the cloud- agnostic data science platform based on Celect’s pDB framework for non-parametric machine learning. From 2013 to 2016, he was the lead algorithms architect at Plume Wifi, a startup focusing on managing home WiFi access points from the cloud. Balaji was responsible for developing machine learning models and algorithms to predict the spatial traffic demands in homes as well as models for predicting interference levels and capacity on different WiFi channels. From 2009 to 2013, he held joint appointments as a researcher at Institute IMDEA networks, and University Carlos III in Madrid, Spain. Balaji received his masters and PhD from the university of Texas at Austin and is a recipient of a Marie-Curie ‘Amarout Europe Programme’ fellowship and TxTEC graduate fellowship.

Find Balaji Rengarajan at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks