What users are saying about

Anaconda

Top Rated
33 Ratings

DataRobot

2 Ratings

Anaconda

Top Rated
33 Ratings
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Score 8.8 out of 101

DataRobot

2 Ratings
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Score 9.3 out of 101

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Likelihood to Recommend

Anaconda

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.
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DataRobot

DataRobot is equipped to serve both cloud computing and on-premise work. Its comprehensive cloud module, channeled through Amazon sustains highly flexible machine learning programs. Lower than average costs are provided via cloud networking because there is no need to install hardware and the additional pricing that comes along with that. DataRobot assists in eliminating hassle by offering various methods of deployment in regard to predictive modeling. The program may not be appropriate for smaller startups of 20 or fewer employees, mainly because the exportable prediction code, and native batch scoring may not translate as well to a smaller firm.
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Pros

  • Anaconda (i.e. Python with lots of packages and the fabulous iPython/Jupyter Notebook) does analytics well. In analytics, or "data science" or whatever buzzword, you have to pick your poison: Python, R, or SAS. Python is the only one that's good at doing other things as well.
  • Like the visualization...The quality of the built-in types of scientific visualization in Python vs. R and their aesthetics is up for grabs. However, Python can do a whole lot of different kinds of visualization above and beyond R. Similarly, JavaScript probably can do more/better visualization than Python, but it's not meant for analytics. Anaconda has enough visualization packages to get you started.
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  • DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
  • Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
  • It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.
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Cons

  • It's still a little buggy. Especially the launcher.
  • It's not always easy to set up. It's not exactly difficult: a Google search away for most things, but silly stuff like path names, installing custom fonts and colors. That kind of thing.
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  • The importance of realizing that most software programming in the Predictive Analysis genre is not 100% ideal cannot be overstated. It's tough to locate a program that would fulfill every need of every business type and size. Though DataRobot is about as close as it gets, it may not be for every industry.
  • Though DataRobot helps to capture six sigma techniques, knowledge, and expertise of the best data scientists in the nation, it may end up causing rifts in intercompany personnel due to fear of job loss on a long term scale.
  • Though fairly priced for a firm of our size and capability, it may be out of market reach for smaller companies at this point. It's important for every firm to understand exactly what they need and can afford.
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Likelihood to Renew

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DataRobot10.0
Based on 1 answer
DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.
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Alternatives Considered

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
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We started off using BigML. A positive about BigML would be that it doesn't require files on your local drive. You just need an internet connection and API allows the user to do anything he or she needs (including model deployment and prediction). We ended up with DataRobot mainly, initially because BigML doesn't provide offline support like other open-source programming does. In the long run DataRobot was much more cost effective as well.
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Return on Investment

  • 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.
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  • DataRobot can run multiple experiments at the same time. This helps to minimize time spent on any given experiment.
  • DataRobot helps to get rid of bottlenecks by yielding various ways to enact completed models of prediction.
  • I consider the Return on Investment high. APIs for real-time scoring have saved us many dollars and time.
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Pricing Details

Anaconda

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

DataRobot

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details