The Dask-ML Workshop Enables your Team to Machine Learning at Scale
Dask-ML Workshop Overview
This half-day workshop introduces participants to Dask-ML for scaling standard Python machine learning tools (e.g., Scikit-Learn, XGBoost). Participants apply various pre-built models on moderate-to-large datasets to learn best practices for parallel & out-of-core machine learning.
We assume participants have prior experience using the Python language and, in particular, using standard Python tools for data analysis (notably NumPy, Pandas, Jupyter). Participants should also have some prior exposure to Scikit-Learn for machine learning and to Dask for scaling data analysis in Python.
At the end of this workshop, participants should be able to:
Deploy incremental learning with partial fit models for large datasets
Exploit parallelism for cross-validation and hyperparameter grid-search using standard Python idioms
Implement parallel prediction using Dask-ML meta-estimators
Scale out linear models (e.g., Linear/Logistic Regressors) & XGBoost with Dask-ML