Tom Augspurger, who works at Anaconda maintaining libraries like pandas, Dask, and Dask-ML, joins Matt Rocklin and Hugo Bowne-Anderson to discuss scalable machine learning in Python.
Dask-ML provides tools for scalable machine learning. It works with libraries like scikit-learn and XGBoost to scale out to larger datasets or larger problems.
We’re fortunate to have great, high-performance libraries like NumPy, SciPy and Scikit-Learn for machine learning. They work great for problems that fit on a single machine. For larger problems, however, you’ll run into compute or memory constraints that slow down the iterative process of developing a machine learning model. Dask-ML let’s you restore that rapid cycle by scaling your familiar machine learning workflow with Dask.
After attending, you’ll know