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Scale EDA & ML Workloads To Clusters & Back With Dask I PyData Chicago January 2022 Meetup
Discover how to scale EDA and ML workloads to clusters and back using Dask, a parallel computing library for Python, and learn how to speed up computations and simulations for data science and scientific computing applications.
- Dask is a parallel computing library for Python that allows users to scale their code up to clusters.
- It can be used to speed up computational tasks by dividing them into smaller ones that can be executed in parallel.
- Dask can be distributed to clusters, such as Kubernetes, and is used in various industries, including finance, broadcasting, and medical research.
- It’s ideal for tasks that require load-balancing and are I/O bound.
- Dask can be used to speed up simulations by splitting them into smaller chunks that can be executed in parallel.
- It’s useful for tasks like data processing, data science, and scientific computing.
- Dask can be used with Jupyter notebooks and is compatible with popular data science libraries such as Pandas and scikit-learn.
- It’s also compatible with other languages, such as R and SQL.
- Dask is scalable and can handle large datasets and distributed computing.
- Dask can be used to distribute any Python code to a cluster, including data science and scientific computing applications.
- It’s used in various domains, including finance, broadcasting, and medical research.
- Dask can be used to speed up simulations, data processing, and data science tasks by dividing them into smaller chunks that can be executed in parallel.
- It’s useful for tasks like data processing, data science, and scientific computing, and is used in various industries, including finance, broadcasting, and medical research.