SARADINDU SENGUPTA

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A “Machine Learning Engineer” by professional title but in reality, I tend to focus more on the architectural design of machine learning systems. I started my professional work building a simple linear regression-based algorithm to forecast solar energy usage. Still, over time I moved from seeing the “machine learning” as the only part to one of the moving parts that should go through the same cycle of test, deploy and integrate phase, effectively employing the concept of continuous integration and deployment.

python machine learning tensorflow time-series forecasting image reconstruction computer vision stremlit statistics quantization tinyml big data

Aktivität
Info

An Analytical Comparison of Different Regularization Parameter Selection Methods for Regularization in Auto-Calibrating Parallel MRI

Vor 4 Monaten • 50 Aufrufe

Solar Energy Output Forecasting from SolarGIS Data for Connected Grid Station

Vor 4 Monaten • 38 Aufrufe

python machine learning tensorflow time-series forecasting image reconstruction computer vision stremlit statistics quantization tinyml big data

SARADINDU SENGUPTA

Präsentationen

GDG Cloud Community Day 2022 - Managing data quality in Machine Learning

PyData Global 2022 - Things I learned while running neural networks on microcontroller

PyData Global 2022 - Lightning Talk - Bessel's Correction

GDG Community Day 2023 - Interpretable ML in production

AZConf 2023 - Considerations for LLMOps: Running LLMs in production

Pydata Global 2023 - How can a learnt model unlearn something

Dokumente

An Analytical Comparison of Different Regularization Parameter Selection Methods for Regularization in Auto-Calibrating Parallel MRI

Solar Energy Output Forecasting from SolarGIS Data for Connected Grid Station