cancel
Showing results for 
Search instead for 
Did you mean: 
Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
cancel
Showing results for 
Search instead for 
Did you mean: 

How do I benefit from parallelisation when doing machine learning?

User16857281869
New Contributor II

There are in principle four distinct ways of using parallelisation when doing machine learning. Any combination of these can speed up the whole pipeline significantly.

1) Using spark distributed processing in feature engineering

2) When the data set that you want to train your model is large and can not be fit into a single machine, you need to use libraries which can natively distribute the training. Spark ML, or Horovod are examples of such libraries

3) You can train many versions of a same model on different datasets all at once using Pandas UDF. Like training a model for many different stores, marketing campagne, sensors and so on

4) You train different models on a same data set by using parallelisation on the hyperparameter search.

1 REPLY 1

sean_owen
Honored Contributor II

Good summary! yes those are the main strategies I can think of.

Connect with Databricks Users in Your Area

Join a Regional User Group to connect with local Databricks users. Events will be happening in your city, and you won’t want to miss the chance to attend and share knowledge.

If there isn’t a group near you, start one and help create a community that brings people together.

Request a New Group