Anaconda - the easiest and quickest way to get going on ML
August 30, 2018

Anaconda - the easiest and quickest way to get going on ML

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Anaconda

We use Anaconda for all of our Machine Learning projects in Data Analytics and Reporting department. Primarily we use Jupyter Notebook, Spyder and RStudio functionality to create various machine learning algorithms to solve real world business problems, such as how to keep users in our game longer and how to better monetize their experience.
  • Everything is in one place, so it's very convinient
  • It's easy to switch between multiple functionalities
  • Performance and Speed - Python and R run smoothly and efficiently.
  • User Interface could be a little bit more clearer.
  • Error messaging can definitely be improved
  • We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
  • We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.
Anaconda is way easier to set-up. On Anaconda we have users working on Machine Learning in minutes, where on PyCharm is takes a lot longer to set-up and often involves getting help from IT. PyCharm is easier to integrate with Code repositories (such as GitHub), so if that's very important to you, you might want to take a look at PyCharm
If your organization is reliant on Machine Learning to solve real world business problems, Anaconda is very well suited for that need. It can be a bit of a pain to install all the necessary dependencies for Python to do Machine Learning. Anaconda takes care of all the installation of appropriate libraries. If you're organization is reliant on GitHub or other code repositories, it's a bit cumbersome to have that in Anaconda, so it might not be the solution for you.