Dask offers Dask-ML services for large-scale machine learning operations using Python. Dask-ML decreases the model training time for medium-sized datasets and experiments with hyperparameter tuning. It offers scikit-learn-like machine learning algorithms for ML operations.
We can scale scikit-learn in three different ways: parallelize scikit-learn using joblib by using random forest and SVC; reimplement algorithms using Dask Arrays using generalized linear models, preprocessing, and clustering; and partner it with distributed libraries such as XGBoost and Tensorflow.
Let's start by looking at parallel computing using scikit-learn.
Parallel computing using scikit-learn
To perform parallel computing using scikit-learn on a single CPU, we need to use joblib. This makes scikit-learn operations parallel computable. The joblib library performs parallelization on Python jobs. Dask can help us perform parallel operations on multiple scikit-learn estimators. Let...
