Most Helpful Review
We're hoping to save costs on internal development but keep enough flexibility to choose ML techniques and...
Find out what your peers are saying about H2O.ai vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
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We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
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.
The ease of use in connecting to our cluster machines.
It is helpful, intuitive, and easy to use. The learning curve is not too steep.
AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms.
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 UI is very user-friendly and that AI is easy to use.
The most valuable feature is data normalization.
The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.
The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.
Visualisation, and the possibility of sharing functions are key features.
It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
When you import the dataset you can see the data distribution easily with graphics and statistical measures.
Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.
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.
I would like to see more features related to deployment.
The model management features could be improved.
It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows.
Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive.
It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O.
When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.
The data cleaning functionality is something that could be better and needs to be improved.
Integration with social media would be a valuable enhancement.
If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.
Operability with R could be improved.
I would like to see modules to handle Deep Learning frameworks.
I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated.
Enable creating ensemble models easier, adding more machine learning algorithms.
Pricing and Cost Advice
We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff.
From a developer's perspective, I find the price of this solution high.
When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly.
To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.
out of 30 in Data Science Platforms
Average Words per Review
out of 30 in Data Science Platforms
Average Words per Review
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Also Known As
|Azure Machine Learning|
H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Learn more about H2O.ai
Learn more about Microsoft Azure Machine Learning Studio
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Software R&D Company38%
Comms Service Provider16%
Financial Services Firm8%
Software R&D Company30%
Comms Service Provider17%