Innovate, differentiate, and modernize with data science and machine learning.
|Top Common Challenges||Solution|
|Setting up interfaces manually to collaborate with peers and connect to data sources and infrastructure||Provides choice to use preferred interface such as Rstudio, Python or Jupyter while providing a common workspace where everyone on the data team can collaborate|
|Coordinating specific dependencies between OS, programming language, and the libraries to build models.||Solves code portability problem by keeping dependencies intact from one environment to another and also does version control|
|Inefficient ad-hoc collaborative methods via emails or slack||Allows continuous collaboration and governed searches of the code, data, and metadata of peers|
|Deployment of enterprise-wide machine learning solutions requires the applications to rapidly scale to accommodate variability in the usage or data||Scales up compute capacity to meet demand and scales down when the usage drops automatically while optimizing the cost based on workload SLA and performance requirements.|
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