For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concerns disappear. You simply provide the training and testing data, and H2O.ai handles everything else, providing the results. It is very easy to select models, and it's a proven AutoML solution that many industries, including insurance and banking, have been using. While many people use Azure AutoML from Azure cloud services, those seeking open source solutions with good support find H2O.ai to be excellent. It's simply a Python library you can import and utilize for numerous tasks. I would recommend it as the best AutoML model. You can explore many features, and based on the results it provides, you can choose the best model to train your data. While I'm not familiar with the cloud-based models, H2O.ai offers a Python library that you can explore and achieve very good results. On a scale of 1-10, I rate this solution a 7.
I would rate the technical support a nine. For organizations considering H2O.ai, my recommendations include appreciating it as a great and flexible tool for machine learning tasks without incurring the high licensing costs associated with alternatives like DataIQ, DataRobot, and DataBricks. Users should fully utilize the AutoML functionality to streamline the model production process significantly. On a scale of 1 to 10, I would rate H2O.ai as a product a nine, as it is a very good product and solution.
It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name private. I'd rate the solution seven out of ten.
H2O is a good product, and I suggest that people use it. My advice to anybody who is considering this type of solution is to consider whether they want to procure such products and use them versus building something custom. It depends on time availability. It really just exposes Spark as the next layer. I would rate this solution a seven out of ten.
It deals well with its core functionality. The product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases. I am using the on-premise version.
Do your due diligence, making sure with your use cases, this is the right product for you. Directionally, they are headed in the right place. They're also putting a lot of muscle behind it, but they're very focused in one area. Supervised on supervised learning is the market that they're going after. If that's their strategy, then they'll get some part of the market, but they'll leave the other part of the market behind. We use just the AWS version of the product. It integrates well with our notebooks. It also integrates well with our homegrown tool sets.
H2O.ai works directly with a lot of our cloud data, big data environment, and Amazon RedShift environment. The big data integration was easier from a performance perspective than Amazon RedShift. That is because our big data environment is still on-premise vs RedShift, which is on the cloud, so we had to go through some struggles to get it operating with RedShift. We also use the on-premise version.
Data Science Platforms empower data analysts to develop, evaluate, and deploy analytical models efficiently. They integrate data exploration, visualization, and predictive modeling in one cohesive environment.These platforms serve as indispensable tools for data-driven decision-making, providing intuitive interfaces and scalable computing power. They enable seamless collaboration between data scientists and business stakeholders, allowing actionable insights to drive strategic initiatives...
For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concerns disappear. You simply provide the training and testing data, and H2O.ai handles everything else, providing the results. It is very easy to select models, and it's a proven AutoML solution that many industries, including insurance and banking, have been using. While many people use Azure AutoML from Azure cloud services, those seeking open source solutions with good support find H2O.ai to be excellent. It's simply a Python library you can import and utilize for numerous tasks. I would recommend it as the best AutoML model. You can explore many features, and based on the results it provides, you can choose the best model to train your data. While I'm not familiar with the cloud-based models, H2O.ai offers a Python library that you can explore and achieve very good results. On a scale of 1-10, I rate this solution a 7.
I would rate the technical support a nine. For organizations considering H2O.ai, my recommendations include appreciating it as a great and flexible tool for machine learning tasks without incurring the high licensing costs associated with alternatives like DataIQ, DataRobot, and DataBricks. Users should fully utilize the AutoML functionality to streamline the model production process significantly. On a scale of 1 to 10, I would rate H2O.ai as a product a nine, as it is a very good product and solution.
It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name private. I'd rate the solution seven out of ten.
H2O is a good product, and I suggest that people use it. My advice to anybody who is considering this type of solution is to consider whether they want to procure such products and use them versus building something custom. It depends on time availability. It really just exposes Spark as the next layer. I would rate this solution a seven out of ten.
It deals well with its core functionality. The product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases. I am using the on-premise version.
Do your due diligence, making sure with your use cases, this is the right product for you. Directionally, they are headed in the right place. They're also putting a lot of muscle behind it, but they're very focused in one area. Supervised on supervised learning is the market that they're going after. If that's their strategy, then they'll get some part of the market, but they'll leave the other part of the market behind. We use just the AWS version of the product. It integrates well with our notebooks. It also integrates well with our homegrown tool sets.
H2O.ai works directly with a lot of our cloud data, big data environment, and Amazon RedShift environment. The big data integration was easier from a performance perspective than Amazon RedShift. That is because our big data environment is still on-premise vs RedShift, which is on the cloud, so we had to go through some struggles to get it operating with RedShift. We also use the on-premise version.