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40 Ratings
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Top Rated
40 Ratings
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Score 8.8 out of 101
5 Ratings
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Score 9.4 out of 101

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Likelihood to Recommend

Anaconda

Anaconda is the best solution when you need to make more basic algorithm training. However, when the client necessity if completely new or there're poor libraries, anaconda is too basic.When designing algorithms, I find ai-one to be very useful. Other tools that more suitable than Anaconda for more complex tasks are protege, biffblue and Nervana Neon
Mauricio Quiroga-Pascal Ortega profile photo

H2O

Use H2O.ai whenever you need easy to use tool, when you must be cost efficient (you can not charge the client extra money for software licenses used), need a tool with lots of algorithms that are normally used in data analytics, or need to work on one machine (it is either not allowed to move data to cloud storage or simply not necessary to connect to Hadoop, etc.). Also, you can call H2O directly from Python which makes analysis more efficient.
Viktor Mulac profile photo

Feature Rating Comparison

Data Exploration

Anaconda
7.5
H2O
Visualization
Anaconda
7.0
H2O
Interactive Data Analysis
Anaconda
8.0
H2O

Data Preparation

Anaconda
7.0
H2O
Interactive Data Cleaning and Enrichment
Anaconda
7.0
H2O
Data Transformations
Anaconda
8.0
H2O
Data Encryption
Anaconda
6.0
H2O
Built-in Processors
Anaconda
7.0
H2O

Platform Data Modeling

Anaconda
5.7
H2O
Automated Machine Learning
Anaconda
4.0
H2O
Single platform for multiple model development
Anaconda
7.0
H2O
Self-Service Model Delivery
Anaconda
6.0
H2O

Model Deployment

Anaconda
4.5
H2O
Flexible Model Publishing Options
Anaconda
5.0
H2O
Security, Governance, and Cost Controls
Anaconda
4.0
H2O

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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  • Excellent analytical and prediction tool
  • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
  • Readily available algorithms, easy to use in your analytical projects
  • Faster than Python scikit learn (in machine learning supervised learning area)
  • It can be accessed (run) from Python, not only JAVA etc.
  • Well documented and suitable for fast training or self studying
  • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
  • It can be used as open source
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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.
Alexander Lubyansky profile photo
  • No weaknesses found yet
  • This is not really a drawback, but rather a warning - the Drivereless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.
Viktor Mulac profile photo

Alternatives Considered

Simple story. I tried both. Canopy felt somehow unintuitive to use.
Alexander Lubyansky profile photo
Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.
Viktor Mulac profile photo

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
  • By using H2O the analyst can focus on analysis itself, not spend too much time with coding etc.
  • Reuse of algorithms and easy model sharing saves time and money
  • An easy learning curve assures low training costs
  • By moving to a paid version, even the Driverless AI, you will still need data scientists and analysts, but maybe not so many!
Viktor Mulac profile photo

Pricing Details

Anaconda

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

H2O

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