Simplified Data Preparation for Machine Learning in Hybrid and Multi Clouds

Cloud storage brings great flexibility in management and cost-efficiency to data scientists, but also introduces new challenges related to data accessibility and data locality for machine learning applications. For instance, when the input data is stored in a remote cloud storage like AWS S3 or Azure blob storage, direct data access is often slow and … Continued

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Why Data Orchestration?

Today’s current pace of innovation is hindered by the necessity of reinventing the wheel in order for applications to efficiently access data. When an engineer or scientist wants to write an application to solve a problem, he or she needs to spend significant effort on getting the application to access the data efficiently and effectively, rather than focusing on the algorithms and the application’s logic.

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