I am currently using the freeware version of Weka on my desktop. As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if there is a commercial product. However, I am aware that the team at Waikato University created MOA (Massive Online Analysis) for data stream analysis, somehow analysis on the fly as new cases get in. They can handle very large datasets with millions of rows, which is beyond the capacity of a desktop. I am not in data streaming so I cannot say much. This online solution is different from what I mentioned above on uploading a machine-learning object into production. To the best of my knowledge, MOA is research, not production.
I like how the classification and prediction work. We should use Weka because the path is very big and much better. If there are a lot more lines of code, then we should use another language.
I use both the paid and the open-source versions of the product. If you're a client and you don't want very many details incorporated in your solution, then we will go full open-source. Open source doesn't have very many solution alignment incorporations. However, the paid version has very many options and stuff that needs to be incorporated when providing a solution. It depends on the specifications of a client which we would use. It's not about the price.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Weka is free and open-source software. That is why I used it over KNIME.
We use the free version now. My faculty is very small.
I am currently using the freeware version of Weka on my desktop. As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if there is a commercial product. However, I am aware that the team at Waikato University created MOA (Massive Online Analysis) for data stream analysis, somehow analysis on the fly as new cases get in. They can handle very large datasets with millions of rows, which is beyond the capacity of a desktop. I am not in data streaming so I cannot say much. This online solution is different from what I mentioned above on uploading a machine-learning object into production. To the best of my knowledge, MOA is research, not production.
The solution is free and open-source.
The solution is open-source and free to use.
I like how the classification and prediction work. We should use Weka because the path is very big and much better. If there are a lot more lines of code, then we should use another language.
Currently, I am using an open-source version so I don't know much about the price of this solution.
I use both the paid and the open-source versions of the product. If you're a client and you don't want very many details incorporated in your solution, then we will go full open-source. Open source doesn't have very many solution alignment incorporations. However, the paid version has very many options and stuff that needs to be incorporated when providing a solution. It depends on the specifications of a client which we would use. It's not about the price.