DataRobot will free your data scientists from the boring part of their job and allow them to focus on the human part
Use Cases and Deployment Scope
We build predictive models with the core supervised learning product. These include attribution models, churn/retention models, segmentation models, and others. Basically, anything that can be accomplished by taking a supervised, labeled set of training data and turning it into a predictive model, we use DataRobot. We have also dabbled with unsupervised learning and time series modeling but have not purchased those packages.
Pros
- Iterative model development
- Fast training of a very large number of models
- Easy deployment to their cloud solution, or export as an approximate model
- Visualization and explanation of important model components
Cons
- We should be able to download data sets from our own projects--after all, we uploaded them originally (and they were not stored locally; they were created specifically for a DataRobot project).
- The sales team is very aggressive at pushing features that we would never use, such as data hygiene (clunky integration of Paxata), ML Ops (just don't need it), and AI services (we're a mature company; we don't need help coming up with use cases).
- Pricing changes every year--not just the amount but what you actually get, so we need to nitpick the contract each year because DataRobot has inevitably eliminated something we need.
Likelihood to Recommend
It's appropriate for speeding up the work of your experienced data scientists. If they spend more than 15% of their time building and tweaking models, DataRobot will cut that down significantly. Caveat emptor: while the DataRobot marketing materials promise to turn any analyst into a data scientist, this is far from the truth. If your potential users do not already understand how machine learning models work, and have not built some models on their own, then they will make mistakes that DataRobot will not correct because it assumes you know what you're doing. Interpreting the results and iterating on models is easy for a trained data scientist but would be baffling for a typical financial analyst.
