Nanda Vijaydev, Director of Solutions Management, BlueData; and Anant Chintamaneni, VP of Products, BlueData
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?
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In this webinar, we’ll dive into a real-world use case and discuss:
- 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
Join this webinar and learn how to get started with a large-scale distributed platform for data science and machine learning.