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

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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.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

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

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • 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

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.