Pramit Choudhary

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I am a seasoned DataScientist. My area of interests is Statistical / Machine Learning modeling( Bayesian and Frequentist Modeling techniques ). In my past life I have lead initiatives and worked on solving problems related to predicting pre-emptive measure to avoid failure for improving operating efficiency in Oil n Gas Industry, social media analysis, recommendation engines, match-making using statistical models, fraud-detection, natural language processing and others. Currently, I am curious about how to efficiently understand the true nature of predictive models and that could lead to better testing and evaluation of the same.

machine learning analytics big data datascience deep learning statistics bayesian learning neural network recommendation engine uci nlp spark optimization

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Pramit Choudhary

Get hands-on with Explainable AI at Machine Learning Interpretability(MLI) Gym!

Model Evaluation in the land of Deep Learning

IE: Named Entity Recognition (NER)

Anomaly detection

Interpretability beyond feature attribution quantitative testing with concept activation vectors (tcav)

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Interpretable Machine Learning

Interpretable machine learning

Making Netflix Machine Learning Algorithms Reliable

Learning to learn - to retrieve information

Model evaluation in the land of deep learning

Production and Beyond: Deploying and Managing Machine Learning Models

Icml2012 tutorial representation_learning

Is that a Time Machine? Some Design Patterns for Real World Machine Learning Systems

Data Workflows for Machine Learning - Seattle DAML

Lessons Learned from Building Machine Learning Software at Netflix

Apache Spark Model Deployment

Convolutional Neural Networks (CNN)

To explain or to predict

10 Lessons Learned from Building Machine Learning Systems