Distributed Data Science and Machine Learning - With Python, R, Spark, & More

Presented by

Nanda Vijaydev, Director of Solutions Management, BlueData; and Anant Chintamaneni, VP of Products, BlueData

About this talk

Implementing data science and machine learning at scale is challenging for developers, data engineers, and data analysts. Methods used on a single laptop need to be redesigned for a distributed pipeline with multiple users and multi-node clusters. So how do you make it work? In this on-demand webinar, hear a real-world use case and learn about: - Requirements and tools such as R, Python, Spark, H2O, and others - Infrastructure complexity, gaps in skill sets, and other challenges - Tips for getting data engineers, SQL developers, and data scientists to collaborate - How to provide a user-friendly, scalable, and elastic platform for distributed data science Learn how to get started with a large-scale distributed platform for data science and machine learning.

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Hewlett Packard Enterprise (HPE) is transforming how enterprises deploy AI / Machine Learning (ML) and Big Data analytics. HPE’s container-based software platform makes it easier, faster, and more cost-effective for enterprises to innovate with AI / ML and Big Data technologies – either on-premises, in the public cloud, or in a hybrid architecture. With HPE, our customers can spin up containerized environments within minutes, providing their data scientists with on-demand access to the applications, data, and infrastructure they need.