Krishi Sharma
where else to find them
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what they talk about
all 4 in the filters
where they keep going back
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never shared a talk
who talks about the same things
- Animesh Singh23 talks
- Jigyasa Grover8 talks
- Tobias Sterbak4 talks
- Giulia Bianchi8 talks
- Erin Mikail Staples6 talks
- Vladimir Osin4 talks
| title | conference | date |
|---|---|---|
| Trust Fall: Three Hidden Gems in MLFlow | PyCon US 2024 | 2024-05-15 |
A concrete recipe: enable autologging so MLflow automatically captures framework-specific parameters and metrics instead of hundreds of scattered log lines… | ||
| Innovation in the Age of Regulation: Federated Learning with Flower | PyData NYC 2023 | 2023-11-01 |
Demonstrates that training without centralizing sensitive records is practical: a demo uses Flower to run a server on port 880, spin up clients running simple… | ||
| Hidden Gems in MLFlow that Improve Experiment Reproducibility | PyData Seattle 2023 | 2023-04-26 |
Shows three concrete MLflow tactics that actually reduce debugging time: use mlflow.autolog() to get consistent parameter and metric captures across branches… | ||
| Plant a Touch-Me-Not: Train Models Without Anyone Touching Your Data with Flower | PyData Seattle 2023 | 2023-04-26 |
Claims use federated learning with Flower to train without moving raw data; evaluation compares PySyft, TensorFlow Federated, and Flower and chooses Flower for… | ||
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