Anjali Datta
Anjali is an MRI Applications Engineer at Vista, a startup combining MRI and AI to shorten wait times for MRI exams, especially in the heart. Before that, she was a postdoc at Stanford Medicine. She also has a PhD in Electrical Engineering from Stanford, during which she developed MRI acquisition and reconstruction methods. Medical imaging is of course a field where ML is taking over, and Anjali is also interested in the applications of deep learning to MRI and other signal processing.
Session
Pandas and scikit-learn have become staples in the machine learning toolkit for processing and modeling tabular data in Python. However, when data size scales up, these tools become slow or run out of memory. Ibis provides a unified, Pythonic, dataframe-like interface to 20+ execution backends, including dataframe libraries, databases, and analytics engines. Ibis enables users to leverage these powerful tools without rewriting their data engineering code (or learning SQL). IbisML extends the benefits of using Ibis to the ML workflow by letting users preprocess their data at scale on any Ibis-supported backend.
In this tutorial, you'll build an end-to-end machine learning project to predict the live win probability after each move during chess games.