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H2O Driverless AI
What Is H2O Driverless AI?
H2O.ai offers an award-winning automatic machine learning platform in Driverless AI and has been recognized as an industry leader in the Forrester New WaveTM: Automation-Focused Machine Learning Solutions, Q2 2019. H2O, open source, is already being used by hundreds of thousands of data scientists and is deployed at over 18,000 organizations across nearly every industry.
Who Uses H2O Driverless AI?
H2O.ai empowers every company to be an AI company. The industrys leading and award winning, H2O Driverless AI makes this possible with 'Make Your Own AI'.
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H2O Driverless AI
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H2O Driverless AI Reviews for Indian Users
Average score
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- 51-200
- 201-1,000
- >1,001
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A very good productivity tool for data science
Comments: From biomarkers, clinical data, HR, finance and manufacturing, any data owner can run native experiments. More advanced users can use the software to increase productivity and prune hypotheses quicky.
Pros:
Drag and drop a dataset, click a button, presto! You have a model training ...
Cons:
The license fee and the lack of pay per use pricing models are a hurdle in any grassroots initiative.
Excellent Product
Pros:
Helps speed up time to production and simplifies the entire ML process.
Cons:
Nothing. The team has been great and the product works very well.
Exceptional Machine Learning Tool
Comments: DAI assists greatly in the automation of our services. It greatly streamlines the process of feature selection, generation, model construction, model testing, and ensemble building and deployment.
Pros:
The performance of DAI is far beyond what can be achieved with tools from Amazon, Google, and Microsoft. A great deal of the human expertise of Kaggle Grandmasters has been incorporated into the product. beyond overall performance, a key advantage of the product is very rapid product development and iteration. When we report an error or make a suggestion for enhancement, a new release is out within weeks.
Cons:
Nothing. The product works extremely well.
Great product
Pros:
Simplified model build and tuning, easy to use, generated doc for experiment that summarizes what was done
Cons:
Wish the price could be more affordable so more people could purchase and use
Alternatives Considered:
Make more with less time
Pros:
We can develop a model in a fraction of the time it would take us using the traditional modelling workflow.
Cons:
Some recent features such as Bring Your Own Recipe are great but still need lots of development to be relevant to some fields.
