Overview
Product Information on H2O AI Cloud
What is H2O AI Cloud?
H2O AI Cloud Pricing
Overall experience with H2O AI Cloud
“Versatile Platform for AI Services”
About Company
Company Description
H2O.ai is concentrated on democratizing artificial intelligence (AI). The main business issue that the organization resolves is the complexity of developing, operating, and innovating AI solutions to address specific business concerns swiftly, thereby expediting the generation of novel concepts. To tackle this, H2O.ai has created H2O Agentic AI and H2O AI Cloud, platforms utilized by organizations globally to simplify AI processes. Not only private sectors but also public and academic organizations trust this organization to fast-track their AI initiatives. H2O.ai is equally committed to promoting Responsible AI and nurturing a strong AI for Good ethos, easing the application and ethical implementation of AI solutions.
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
Reviewer Insights for: H2O AI Cloud
H2O AI Cloud Likes & Dislikes
- Has extensive automation capabilities - Rapid prototyping and quick model building - Integrates with various platforms
The platform offers an intuitive and user friendly interface which enables our team members to quickly learn and adapt to the platform. It also provides an extensive range of libraries of ML algorithms which empowers us to experiment with various models to find the best fit for a business challenges.
The quick iteration for developing models. I get to focus on pulling the data I need and setting up the problem. Then I know H2O will take care of all possible modeling styles to reach the best solution. The organization of model experiments. I no longer loose track of my best model setups as I iterate. Everything is stored clearly with the training dataset and parameters. The reports and analysis gives me all of the best metrics and charts for evaluation without having to manually code them.
- Primarily tailored for large enterprises - High cost
Running complex models and larger datasets can sometimes consume a significant amount of computational resources. While the platform is quite versatile ,integrating it with our systems required various changes in software architectures.
The pricing can be confusing. It is currently based on AI-Units which relate to some level of compute memory but I am never quite sure exactly what 1 AI Unit is. The different parts of the H2O ecosystem are all slightly separated. So you have to go to different logins and windows to reach Driverless vs ML Ops, etc. ML Ops does not give back the clearest errors when you are passing the wrong datatype in the JSON for scoring.
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Peer Discussions
H2O AI Cloud Reviews and Ratings
- SR. DATA SCIENTIST10B+ USDIT ServicesReview Source
Versatile Platform for AI Services
Its a versatile platform which empowers businesses to implement AI solutions efficiently. - LEAD ANALYST1B-10B USDRetailReview Source
Comprehensive solution for deployment and management of AI models
H2 AI cloud has facilitated development , deployment and management of AI models and applications for our organization. It provides cloud based environment with tools for data preprocessing, model training and deployment which makes it easy to integrate AI capabilities into business processes. - Senior Data Science Manager50M-1B USDEnergy and UtilitiesReview Source
H2O Driverless has made our department at least 3-5x more efficient. 10/10
H2O Driverless has blown me away. I have always been skeptical of auto-ML solutions for the past 10 years as I have seen them come around. It always felt like too much of a trade-off in customization. H2O has completely changed my perspective. The pre-sales process was wonderful with a demo period with regular support from their engineers and sales team. The dataset analytics gives me instant insights and confidence that I have not missed any glaring issues or data leakage. It also suggests all of the best log/exponential transformations for different columns in my data. The Driverless AI itself is incredible. It takes a huge amount of the tuning away from my hands. And I know it is implementing modeling best practices even better than I ever could with my own implementation in Python. The organization of model experiments and projects is a game-changer for production and iterations. The ML Ops model deployment is also so easy and powerful to use. I no longer have to worry about how to deploy a model into production. I just host it on an endpoint and then can either have applications request scores from it for single requests or batch score my model every night and save it into our database. Having it track data drift and other issues gives me more peace of mind that we are not having things go awry behind the scenes. - Analyst10B+ USDBankingReview Source
AutoML = H2O, great UI, customer support and model development
Its a easy to use software with good documentation available online. - Senior Data Scientist50M-1B USDFinance (non-banking)Review Source
DriverlessAI & Exceptional Data Science Expertise.
H2O, as a whole, is a tremendous company. Being a Senior Data Scientist I find the entire stack unparalleled. The known pieces of using DriverlessAI; auto feature engineering, ease of deployment, high model ROC, PR, Log-Loss, etc... are well documented. The modeling is fantastic, but the customer service & documentation are what I believe to be the greatest asset. Other Data Scientists will agree that you can often end up stuck on a bug, feature transformation, deployment question, and the list goes on and on. We are forced to search StackOverflow & the internet as a whole sometimes for many hours trying to find an answer. With H2O's customer service you have another team of Data Scientists who have either had this identical problem, or have ran into something similar. Your time to solving your issue is vastly expedited. Sure, DriverlessAI is awesome, but having other expert Data Scientists to bounce bugs off of is what makes H2O special.