Best data science tools for modelling and deploying machine learning and predictive algorithms

Some of the best data science platforms available for modelling and deploying machine learning and advanced analytics


Platforms which allow data scientists to build and deploy algorithms are increasingly important as businesses look to operationalise their data faster than ever before.

Gartner defines data science platforms simply as "engines for creating machine-learning solutions". For the sake of this article we have broadened Gartner's definition to include everything from data science workbenches, where teams can collaborate on code and deploy it themselves, to guided data science solutions.

See also: How to get a job as a data scientist: What qualifications and skills you need and what employers expect

It is important to remember that all data science platforms are relatively immature and none are a silver bullet. "Data science is not plug and play," Matt Jones, lead analytics strategist at Tessella Analytics told Computerworld UK. "Platforms are fine, but they need to be trained by someone who understands the data and the context it exists in. If you’re outsourcing data science to a tech vendor, be absolutely sure they understand your business and your data."

With that in mind, here are some of the best and most popular data science platforms, from open source to established vendors, being used by enterprises today.

Our top picks are:

  • Microsoft Azure machine learning platform
  • Domino Data Lab
  • Cloudera Data Science Workbench