What is our primary use case?
Building predictive models, including customer churn and lead generation.
Performance has been great. I've used it for about eight years or so, lots of flexibility. It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms.
We aren't putting that many machine-learning models into production. This is not the primary tool we use. This is more for me in terms of data exploration and knowledge discovery, that kind of thing. I really haven't done any production models in my current role. In previous roles I have.
In terms of cloud environments, it's actually a combination. Long story, but it's a combination of different things.
It's more for data, as a data repository.
My experience so far using Modeler is good. I haven't noticed any issues with our current solution.
What is most valuable?
I don't use it for governance and security issues or for visual modeling. For data visualization we use ThoughtSpot, Tableau, Power BI. In terms of the graphic capability, those are existing platforms that have a larger user base, so it's unlikely that we'll use Modeler exclusively for data visualization.
What needs improvement?
I really can't think of anything off the top of my head because I feel like I'm under utilizing it as it is, because we're doing specific things. Two or three years ago, I would've said R and Python integration, but they've done that.
For how long have I used the solution?
More than five years.
What do I think about the stability of the solution?
I've been using it since it was called Clementine. Every version seems to be better than the previous, but I don't think I've ever had any catastrophic failures, or any bugs that were significant enough to not have a work-around available.
What do I think about the scalability of the solution?
The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool.
How is customer service and technical support?
I haven't used tech support recently. We used IBM designates for things like training and the like, which has always been very good, but I can't really think of any issue that required any technical support.
What other advice do I have?
It's a solution that was available when I entered the role. I have heard from others who were in the process of trying to start from ground-zero, and the tendency for them is to go with open-source because of the revenue model, obviously.
I would say, if you're considering that open source-solution, definitely consider Modeler as well. Put together some kind of proposal that allows you to figure out how much time it's going to take individual people to create those models, versus being able to have an out-of-the-box solution that gets your team going more immediately.
Support is another benefit of going with Modeler over open-source. SPSS has been around for a long time. IBM acquired them, and they've added functionality and features to meet the needs of growing data science populations.