As businesses around the world are embarking on building innovative solutions, we’re seeing a growing trend adopting data science workloads across various industries. Recently, we’ve seen a greater push towards reducing the friction between data engineers and data scientists. Data scientists are now enabled to run their experiments on their local machine and port to it powerful clusters that can scale without rewriting the code.
You have many options for running data science workloads, such as running it on your own managed Spark cluster. Alternatively there are cloud options such as Amazon SageMaker, Amazon EMR and Amazon Elastic Kubernetes Service (Amazon EKS) clusters. We’re also seeing customers adopting Dask—a distributed data science computing framework that natively integrates with Python libraries such as Pandas, NumPy, and Scikit-learn machine learning (ML) libraries. These libraries were developed in Python and originally optimized for single-machine processing. Dask was developed to help scale these widely

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