Your team has built the data science models. You've showcased the results to the business and they are sold. Now you've got to make sure that your model scales to meet the rigorous performance targets. Should you buy bigger machines? Should you move to Hadoop?
Big Data, advanced analytics and scientific computing bring exciting opportunities for businesses like yours to leverage. However, it also creates serious computational challenges. To effectively manage the increasing complexity, your Data Science team needs technologies that will easily scale and take advantage of the available processing power.
In this whitepaper, you'll learn from our seasoned experts about the approaches to scaling your data science models. We'll review the various options and show you how to easily accomplish them using Anaconda, the leading Open Data Science platform powered by Python. We will:
Define the computing constraints and identify techniques for resolving them
Describe ways to deliver high performance on today's architectures
Showcase the flexibility of the Anaconda platform to scale with any modern infrastructure