Innovate, differentiate, and modernize with data science and machine learning.
|Top Common Challenges
|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.
Data Scientists can build, deploy and iterate on their models faster with
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