Presentation: Transmogrification: The Magic of Feature Engineering

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 10:40am - 10:50am

Day of week: Tuesday

Share this on:

Abstract

Machine learning algorithms often take center stage in machine learning and AI. However, in the real world, 90% of the time spent building models goes into creating the mythical perfect numeric matrix of features, to feed into the chosen algorithm. Every machine learning team repeats the same effort, reinventing the wheel once again.

In this session, you'll learn about transmogrification, where we magically and automatically engineer features based on the type of feature, data distribution and association with the response variable.

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: Mayukh Bhaowal

Director of Product Management @Salesforce

Mayukh Bhaowal is a Director of Product Management at Salesforce Einstein, working on automated machine learning. Mayukh received his Masters in Computer Science from Stanford University. Prior to Salesforce, Mayukh worked at startups in the domain of machine learning and analytics. He served as Head of Product of a ML platform startup, Scaled Inference, backed by Khosla Ventures, and led product at an ecommerce startup, Narvar, backed by Accel. He was also a Principal Product Manager at Yahoo and Oracle.

Find Mayukh Bhaowal at

Speaker: Leah McGuire

Principal Member of Technical Staff @Salesforce

Leah McGuire is a Principal Member of Technical Staff at Salesforce, working on automating as many of the steps involved in machine learning as possible. Before joining Salesforce, Leah was a Senior Data Scientist on the data products team at LinkedIn. She completed a PhD and a Postdoctoral Fellowship in Computational Neuroscience at the University of California, San Francisco, and at University of California, Berkeley, where she studied the neural encoding and integration of sensory signals.

Find Leah McGuire 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.