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

Anonymous | TrustRadius Reviewer
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. (Driverless AI): several test models did not complete, and 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?


Are you happy with DataRobot's feature set?


Did DataRobot live up to sales and marketing promises?


Did implementation of DataRobot go as expected?


Would you buy DataRobot again?


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.

DataRobot Feature Ratings

Connect to Multiple Data Sources
Extend Existing Data Sources
Automatic Data Format Detection
MDM Integration
Not Rated
Interactive Data Analysis
Interactive Data Cleaning and Enrichment
Not Rated
Data Transformations
Data Encryption
Not Rated
Built-in Processors
Multiple Model Development Languages and Tools
Automated Machine Learning
Single platform for multiple model development
Self-Service Model Delivery
Flexible Model Publishing Options
Security, Governance, and Cost Controls
Not Rated