I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was evolving and had open-source libraries available for Python. I started exploring that area, and rather than working on independent or individual models, H2O.ai provided better flexibility where I could examine more models and obtain results. Based on these results, I could make the next set of decisions. With the job portal data, I was initially concerned about several models, but when I examined the dataset results from H2O.ai, I received a decent analysis which helped me finalize the model.
Normally, I use H2O.ai for my machine learning tasks, and to give an example, some of the models that I've created using H2O.ai are taxi demand forecasting and a scoring model for leads. Most of my use cases are around running machine learning algorithms on the data and then producing some sort of predictions. I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model. AutoML chooses the right model for your data and does the rest itself. AutoML has been a very powerful feature in many high-end tools, such as DataRobot and DataBricks, but H2O.ai has had the AutoML feature for a long time. Although I come from a data science background and prefer to evaluate each of my models myself, I've used AutoML in H2O.ai, and I think it's a great feature to have. AutoML reduces the time and expertise needed for developing machine learning models by approximately 50 or 60%. In traditional processes, I would choose five or six models, train each model, and then assess them based on one or multiple metrics, which can be very time-consuming. With AutoML automating that process and utilizing a large repository of models fitting on the data, it immensely reduces the time spent. Prior to AutoML, data scientists usually had four or five models in mind, but now AutoML uses more models, making it clear that a data scientist versus AutoML cannot be compared, as AutoML definitely reduces the amount of time spent on model creation. I once worked on a model for anomaly detection, using H2O.ai to predict anomalies in terms of payments made, determining whether a payment was fraudulent. Another example includes the taxi demand prediction model, which predicts how many taxis are needed at different shopping malls in Dubai. For instance, it would predict that tomorrow at 10 a.m., 100 taxis will be needed at Dubai Mall. These are examples where H2O.ai has helped organizations make decisions based on the model output.
I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them. There are various use cases for this solution. One of the ones I worked on was a trailer forecasting solution. The customer wanted to understand the preload capacity that would be needed to have on hand so that they could call upon the right sized trailers and the right packages. It was a problem of logistics where you had to determine how many trailers were required in order to ship the packages being transported, and also have them ready just in time.
The idea is to migrate the current model's development practice to another platform. Then after, try to create a proprietary platform using R and Python. The company is interested in using an external platform in order to have an updated environment.
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...
I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was evolving and had open-source libraries available for Python. I started exploring that area, and rather than working on independent or individual models, H2O.ai provided better flexibility where I could examine more models and obtain results. Based on these results, I could make the next set of decisions. With the job portal data, I was initially concerned about several models, but when I examined the dataset results from H2O.ai, I received a decent analysis which helped me finalize the model.
Normally, I use H2O.ai for my machine learning tasks, and to give an example, some of the models that I've created using H2O.ai are taxi demand forecasting and a scoring model for leads. Most of my use cases are around running machine learning algorithms on the data and then producing some sort of predictions. I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model. AutoML chooses the right model for your data and does the rest itself. AutoML has been a very powerful feature in many high-end tools, such as DataRobot and DataBricks, but H2O.ai has had the AutoML feature for a long time. Although I come from a data science background and prefer to evaluate each of my models myself, I've used AutoML in H2O.ai, and I think it's a great feature to have. AutoML reduces the time and expertise needed for developing machine learning models by approximately 50 or 60%. In traditional processes, I would choose five or six models, train each model, and then assess them based on one or multiple metrics, which can be very time-consuming. With AutoML automating that process and utilizing a large repository of models fitting on the data, it immensely reduces the time spent. Prior to AutoML, data scientists usually had four or five models in mind, but now AutoML uses more models, making it clear that a data scientist versus AutoML cannot be compared, as AutoML definitely reduces the amount of time spent on model creation. I once worked on a model for anomaly detection, using H2O.ai to predict anomalies in terms of payments made, determining whether a payment was fraudulent. Another example includes the taxi demand prediction model, which predicts how many taxis are needed at different shopping malls in Dubai. For instance, it would predict that tomorrow at 10 a.m., 100 taxis will be needed at Dubai Mall. These are examples where H2O.ai has helped organizations make decisions based on the model output.
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.
I am a solution architect and a consultant, and I use H2O as a machine learning platform. I create ensemble models using R and H2O, tune the hyperparameters, and then deploy them. There are various use cases for this solution. One of the ones I worked on was a trailer forecasting solution. The customer wanted to understand the preload capacity that would be needed to have on hand so that they could call upon the right sized trailers and the right packages. It was a problem of logistics where you had to determine how many trailers were required in order to ship the packages being transported, and also have them ready just in time.
The idea is to migrate the current model's development practice to another platform. Then after, try to create a proprietary platform using R and Python. The company is interested in using an external platform in order to have an updated environment.
We use it for building models with large amounts of data.
Our primary use case is machine learning.
Our primary use case is for data science. Some of our data scientists use it pretty heavily to build models.