What users are saying about
Top Rated
41 Ratings
4 Ratings
Top Rated
41 Ratings
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Score 8.7 out of 101
4 Ratings
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Score 8.8 out of 101

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Likelihood to Recommend

Anaconda

Anaconda is great for academic and private organizations that cannot afford more expensive Python/R package managers. Also, it is more appropriate for intermediate to advanced Python users--Anaconda can be somewhat frustrating for beginners, as it takes some practice to get comfortable with the workflow. I find it particularly useful for working in teams, because if everyone uses the same package manager, it is easier to troubleshoot issues and makes for reproducible research. For wealthier organizations, a premium package management system (with tech support) would be ideal. Anaconda is also great for people working independently on code development.
Maike Holthuijzen profile photo

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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Feature Rating Comparison

Data Exploration

Anaconda
7.5
DataRobot
Visualization
Anaconda
7.0
DataRobot
Interactive Data Analysis
Anaconda
8.0
DataRobot

Data Preparation

Anaconda
7.0
DataRobot
Interactive Data Cleaning and Enrichment
Anaconda
7.0
DataRobot
Data Transformations
Anaconda
8.0
DataRobot
Data Encryption
Anaconda
6.0
DataRobot
Built-in Processors
Anaconda
7.0
DataRobot

Platform Data Modeling

Anaconda
5.7
DataRobot
Automated Machine Learning
Anaconda
4.0
DataRobot
Single platform for multiple model development
Anaconda
7.0
DataRobot
Self-Service Model Delivery
Anaconda
6.0
DataRobot

Model Deployment

Anaconda
4.5
DataRobot
Flexible Model Publishing Options
Anaconda
5.0
DataRobot
Security, Governance, and Cost Controls
Anaconda
4.0
DataRobot

Pros

  • Anaconda itself already carries the most popular Python packages so for most developers it is sufficient enough to deal with the normal work requirements.
  • The Jupyter Notebook is a very encouraging feature which allows the researcher to apply the data analysis in an intuitive way. It provides step by step understanding the data, processing the data, visualizing the data and trying out the different methodology and algorithm
  • Both the old version of Python and the new version of Python are supported, giving a very good backward compatibility of some old Python codes developed beforehand.
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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

  • Although some other users mentioned the installation is "simple", we did encounter some challenge in a highly controlled environment (due to security reasons).
  • Jupyter Notebook is extremely slow when the client/server side of the network's speed/bandwidth is not balanced.
  • Bootstrapping Anaconda takes too long, sometimes I even started doubting it would respond any more.
  • If there are extra python packages you need but are not by default installed by Anaconda, then some efforts will be required to figure out how to put them in the right place.
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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

No score
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No answers on this topic
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 the best Python environment because you have all the things you need all in one places, at the reach of your hand. You can download and manage libraries as you wish and is very easy to create new projects and API's for all your stuff.It's Multiplatform so you don't have to take care to use your stuff in another computer. I think it is the best characteristic.
Alejandro Daniel Copati profile photo
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 save a lot of programming time, since all the add-ons are in the same environment.
  • Being multiplatform, Anaconda is perfect for work teams with many people, since it supports all operating systems.
  • The only negative thing that can happen is that at first, the way of working in Anaconda can be a bit confusing. Once passed the learning period, its daily use is very comfortable.
Alejandro Daniel Copati profile photo
  • 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