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Alessandra Bilardi - From your laptop to all resources you need by your Jupyter notebook | PyData
Introducing Dask and AWS: Scale machine learning processes with parallel processing and cloud computing, configuring clusters and managing resources to optimize efficiency.
- Alessandra Bilardi introduces Dask, a library for parallel processing in Python, and AWS, a cloud computing platform, as tools to improve the efficiency of machine learning processes.
- The speaker configures AWS credentials and creates a Dask cluster, explaining the importance of scaling the number of workers depending on the number of CPUs available.
- The tutorial covers the use of Shift-learn, a library for random search and cross-validation, and Joblib, a library for parallelizing computations.
- The speaker introduces the concept of Dask cluster, which is used to create a distributed computing environment, and explains how to configure AWS credentials and create a Dask cluster.
- The tutorial shows how to use Dask and Shift-learn to parallelize machine learning computations, including data loading, model initialization, and hyperparameter tuning.
- The speaker highlights the importance of managing resources when using cloud computing, including CPU and memory allocation.
- The tutorial concludes with a demonstration of how to use Dask and Shift-learn to improve the efficiency of machine learning processes on AWS.