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Purushothaman Saravanan | Darth Kronos

Greetings! I’m Purushothaman, an AI enthusiast with a passion for accelerating the entire AI pipeline, from training to inference. My enthusiasm extends to on-device AI and distributed training paradigms. I firmly believe in the power of decentralization, where every device contributes to AI training, fostering a more inclusive and scalable AI ecosystem. From fine-tuning machine learning models for computer vision to optimizing large language models, I find joy in pushing the boundaries of AI deployment strategies. Join me on this exhilarating journey as we harness the potential of AI acceleration, paving the way for smarter, more efficient technologies that seamlessly integrate into our everyday lives.

Skills

  • Python
  • PyTorch
  • MLflow
  • Prefect
  • Terraform
  • TensorRT
  • Docker
  • AWS

Connect with me

Purushothaman Saravanan's Projects

awesome-dynamodb icon awesome-dynamodb

List of resources for learning about modeling, operating, and using Amazon DynamoDB

crop-yield-prediction icon crop-yield-prediction

Machine learning (ML) models were engineered after processing agricultural data and Regression models were deployed to predict the yield of 4 major crops. The developed models were then compared with each other to obtain the best performing model.

cross-lingual-ser icon cross-lingual-ser

A cross-lingual SER that can learn language invariant representations without requiring target-language data labels

pidnet icon pidnet

TensorRT implementation for PIDNet

spectrum-sensing icon spectrum-sensing

Using signal processing based features to train and validate machine-learning algorithms to improve spectrum sensing and related problems in cognitive radios.

unsupervised-domain-adaptation icon unsupervised-domain-adaptation

Empirical evaluation and analysis of state-of-the-art methods for unsupervised domain adaptation on OFFICE-31 dataset, a benchmark dataset for visual domain adaptation.

visualizing-cnn icon visualizing-cnn

Visualization technique that gives insight into the function of intermediate feature layers and the operation of a Convolutional Neural Network.

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