G2M Platform vs. H2O.ai

Creating impact analysis when there are multiple levels of categories for comparison, i.e., firmographics, geo, etc.

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.

Incentivized

• Data Insights
• Machine Learning
• Drive Growth

• AutoML
• Bigdata support with H2O's Sparkling Water

Incentivized

• More Visuals
• Better Data Hygiene Features

• 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.

Incentivized

The overall experience I have with H2O is really awesome, even with its cost effectiveness.

Incentivized

Analyzr gives more transparency than the others tools we have used, allowing us to see the actual model and data insights instead of a black box approach. The tool is also more intuitive then others, allowing members with limited Python, R coding to create models.

I have used Knime, RapidMiner, and Weka before I heard about H2O, but amongst all I really liked H2O. However, nowadays Googles AutoML and AWS SageMaker AutoML platform are really competitive, but more costly than H2O.

Incentivized

• Identified a 30% lift in certain Marketing Efforts, justifying the continuing spend here.
• Clustering allows us to target specific customers for upgrade, resulting in a top 10% to focus sales and marketing resources on.

• Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
• Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
• Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product

Incentivized