Workshop: Escaping MemoryError- Machine Learning on Big Data with Dask
In this hands-on workshop, attendees will be introduced to Dask, a Python-native parallel computing framework. Dask extends traditional Python tools to operate at scale across a cluster of machines, removing memory and compute limitations. Instructors will walk step-by-step through setting up a Dask cluster, processing large datasets efficiently, and performing machine learning model training across the cluster.
The workshop will take place within Saturn Cloud Hosted, a platform that enables quick and easy access to parallel computing in Python. Attendees will receive free credits to Saturn Cloud Hosted to facilitate learning during and after the session. Join us to get hands-on experience using Saturn Cloud and learn what you need to confidently scale up your own machine learning.
After this workshop you will know:
What Dask is and how it fits in with the broader PyData ecosystem
When to use Dask to scale out machine learning workloads
How to use Dask Dataframes for loading and cleaning data
How to perform distributed model training with Dask
To get the full learning value from this workshop, attendees should have prior experience with machine learning in Python. Experience with parallel computing is not needed.
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