Presentation: The Black Swan of Perfectly Interpretable Models

Track: Predictive Data Pipelines & Architectures

Location: Cyril Magnin I

Duration: 11:00am - 11:50am

Day of week: Tuesday

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Abstract

Machine Learning (ML) software differs from traditional software in the sense that outcomes are not based on a set of hand-coded rules and hence not easily predictable. The behavior of such software changes over time based on data and feedback loops. At Salesforce Einstein, we care deeply about building trust and confidence in such intelligent software programs. Why does a particular email have a higher likelihood of being opened than another? What are the shapes and patterns in the dataset, which lead to certain predictions? And can such insights be actionable?

As machine learning pervades every software vertical, and is increasingly used to automate decisions, model interpretability becomes an integral part of the ML pipeline, and can no longer be an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy and other model evaluation metrics.

This talk will discuss the steps taken at Salesforce Einstein towards making machine learning transparent and less of a black box. We will explain how interpretability fits into the ML data pipeline, what we learned trying different approaches and how it has helped drive wider adoption of ML software.

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

Proposed Tracks

  • Real-World Data Engineering

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

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.