Domino Enterprise MLOps Platform vs. H2O.ai

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Domino Enterprise MLOps Platform
Score 8.0 out of 10
Enterprise companies (1,001+ employees)
The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality and impact of data science at scale. Domino is presented as open and flexible, to empower professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Domino Enterprise MLOps…N/A
H2O.ai
Score 6.3 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.N/A
Pricing
Domino Enterprise MLOps PlatformH2O.ai
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Domino Enterprise MLOps PlatformH2O.ai
Free Trial
YesNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Domino Enterprise MLOps PlatformH2O.ai
Top Pros

No answers on this topic

Top Cons

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Domino Enterprise MLOps PlatformH2O.ai
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User Ratings
Domino Enterprise MLOps PlatformH2O.ai
Likelihood to Recommend
-
(0 ratings)
8.1
(3 ratings)
Support Rating
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Domino Enterprise MLOps PlatformH2O.ai
Likelihood to Recommend
Domino Data Lab
No answers on this topic
H2O.ai
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.
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Pros
Domino Data Lab
No answers on this topic
H2O.ai
  • Excellent analytical and prediction tool
  • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
  • Readily available algorithms, easy to use in your analytical projects
  • Faster than Python scikit learn (in machine learning supervised learning area)
  • It can be accessed (run) from Python, not only JAVA etc.
  • Well documented and suitable for fast training or self studying
  • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
  • It can be used as open source
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Cons
Domino Data Lab
No answers on this topic
H2O.ai
  • Better documentation
  • Improve the Visual presentations including charting etc
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Support Rating
Domino Data Lab
No answers on this topic
H2O.ai
The overall experience I have with H2O is really awesome, even with its cost effectiveness.
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Alternatives Considered
Domino Data Lab
No answers on this topic
H2O.ai
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.
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Return on Investment
Domino Data Lab
No answers on this topic
H2O.ai
  • 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
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ScreenShots

Domino Enterprise MLOps Platform Screenshots

Screenshot of The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality and impact of data science at scale.Screenshot of The Self-Service Infrastructure Portal makes data science teams more productive with access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming DevOps tasks, data scientists can focus on the tasks at hand.Screenshot of The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle.Screenshot of The System of Record has a reproducibility engine, search and knowledge management, and integrated project management. Teams can find, reuse, reproduce, and build on any data science work to amplify innovation.Screenshot of Model monitoring capabilities ensure that all production models maintain peak performance. Automated alerts provide notification when data and quality drift occurs so users can re-train, rebuild, and re-publish the model.Screenshot of Nexus is a single pane of glass to run data science and ML workloads across any compute cluster — in any cloud, region, or on-premises. It unifies data science silos across the enterprise, providing one place to build, deploy, and monitor models.