Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.
$0
per month
H2O.ai
Score 6.6 out of 10
N/A
An open-source end-to-end GenAI platform for air-gapped, on-premises or cloud VPC deployments. Users can Query and summarize documents or just chat with local private GPT LLMs using h2oGPT, an Apache V2 open-source project. And the commercially available Enterprise h2oGPTe provides information retrieval on internal data, privately hosts LLMs, and secures data.
Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
User friendliness: This is by far the most user friendly tool I've seen in analytics. You don't need to know how to code at all! Just create a few blocks, connect a few lines and you are capable of running a boosted decision tree with a very high R squared!
Speed: Azure ML is a cloud based tool, so processing is not made with your computer, making the reliability and speed top notch!
Cost: If you don't know how to code, this is by far the cheapest machine learning tool out there. I believe it costs less than $15/month. If you know how to code, then R is free.
Connectivity: It is super easy to embed R or Python codes on Azure ML. So if you want to do more advanced stuff, or use a model that is not yet available on Azure ML, you can simply paste the code on R or Python there!
Microsoft environment: Many many companies rely on the Microsoft suite. And Azure ML connects perfectly with Excel, CSV and Access files.
It is easier to learn, it has a very cost effective license for use, it has native build and created for Azure cloud services, and that makes it perfect when compared against the alternatives. As a Microsoft tool, it has been built to contain many visual features and improved usability even for non-specialist users.
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.
Productivity: Instead of coding and recoding, Azure ML helped my organization to get to meaningful results faster;
Cost: Azure ML can save hundreds (or even thousands) of dollars for an organization, since the license costs around $15/month per seat.
Focus on insights and not on statistics: Since running a model is so easy, analysts can focus more on recommendations and insights, rather than statistical details
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
