We performed a comparison between H2O.ai and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The ease of use in connecting to our cluster machines."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"The solution facilitates our production."
"It helps in building customized models, which are easy for clients to use."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"The solution is very fast and simple for a data science solution."
"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"I would like to see more features related to deployment."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"The model management features could be improved."
"Operability with R could be improved."
"The speed of deployment should be faster, as should testing."
"Microsoft Azure Machine Learning Studio worked okay for me, so right now, I don't have any room for improvement in mind for it. What I'd like added to Microsoft Azure Machine Learning Studio in its next version is a categorization for use cases or a template that makes the use cases simple to map out, for example, for healthcare, medical, or finance use cases, etc. This would be very helpful for users of Microsoft Azure Machine Learning Studio, especially for beginners."
"The product must improve its documentation."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"Technical support could improve their turnaround time."
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H2O.ai is ranked 20th in Data Science Platforms while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 50 reviews. H2O.ai is rated 7.6, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku Data Science Studio, KNIME and IBM Watson Studio, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform.
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