DataRobot will free your data scientists from the boring part of their job and allow them to focus on the human part
August 04, 2022
DataRobot will free your data scientists from the boring part of their job and allow them to focus on the human part
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with DataRobot
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.
- 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
- 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.
- Major increase in productivity because we're no longer building models "by hand"
- Peace of mind once models are built that they won't break
- Ability to rapidly test out ideas that may or may not have machine learning solutions
We only use the core product that DataRobot originally offered. Since their inception, they have acquired and integrated various companies for various purposes: ML Ops, Neutonian for time series model, Paxata for data hygiene, and smaller ones. We have tried several of these out but didn't find that they were up to the level of the core supervised learning product, so we don't use them. DataRobot is a plug-and-play system for us, and it needs to be, should their prices get out of control--in this way, we can just plug in a competitor without a massive transition cost.
Building models quickly, deploying them quickly and having peace of mind that they will continue running without major issues (outside of data drift, which we monitor). Marketing has transformed into an exercise in building the best predictive models to target a [potential] customer base, and DataRobot plugs into the modeling part very easily.
We consistently return to DataRobot for its ease of use and ability to get the job done without major hurdles. Thus far, we just haven't found that in other products.
H2O.ai (Driverless AI): several test models did not complete, and H2O.ai team could not explain why.
Sagemaker: Much easier if you're already experienced with the AWS ecosystem; otherwise, good luck, you'll need it.
Kortical: Not quite ready for prime time, though we liked the direction they were going
KNIME: great for data analytics, not so deep on the modeling side
H2O.ai (Driverless AI): several test models did not complete, and H2O.ai team could not explain why.
Sagemaker: Much easier if you're already experienced with the AWS ecosystem; otherwise, good luck, you'll need it.
Kortical: Not quite ready for prime time, though we liked the direction they were going
KNIME: great for data analytics, not so deep on the modeling side
Do you think DataRobot delivers good value for the price?
Yes
Are you happy with DataRobot's feature set?
Yes
Did DataRobot live up to sales and marketing promises?
Yes
Did implementation of DataRobot go as expected?
Yes
Would you buy DataRobot again?
Yes