Hi, You also raise some interesting points...
Weka has integrations to Hadoop and Spark via 'plugins' from their package manager. And Weka's 'sister-project', MOA is specifically intended to operate on large data streams. These products are extensive, open-source and extendible. Regarding Python Notebooks - we can work with Weka's library in Python or even Java in Notebooks ( https://waikato.github.io/weka-wiki/jupyter_notebooks/ <https://waikato.github.io/weka-wiki/jupyter_notebooks/> ). I have played a little with this and it works great. I also wondered whether a workbench for Ignite ML would maybe be better integrated into the Ignite Web Console. But looking at the extent of work required to provide the kind of functionality and visualization already available in Weka - one wonders if it isn't more rational to work on integration between the projects? Weka, being a Java application is of course also capable of running on any platform that Java supports, so it has great portability. I think that the work that the Ignite team are doing on ML is based on the excellent concept of DML (distributed machine learning), which is extremely important for future scalability. And it seems to have good momentum as seen by the number of features being added to the new, upcoming 2.8 release. All the development so far has understandably been focused on the underlying ML 'infrastructure'. The challenge though is that in order to stimulate adoption, we need practical interactive environments. Really looking forward to the evolution of ML on Ignite. And Kudos to the talented team behind it. Cheers, Jose -- Sent from: http://apache-ignite-users.70518.x6.nabble.com/
