DataRobot Provides State-of-the-Art ML Automation
December 25, 2021

DataRobot Provides State-of-the-Art ML Automation

Nathan Patrick Taylor | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with DataRobot

We use DataRobot to address several uses cases across clinical, financial, and operational areas. In the clinical setting, we are looking to predict specific outcomes, like readmissions, in an effort to adjust reduce the readmission rate. The operations team uses outputs from DataRobot to forecast census levels that potentially impact our financials.

Pros

  • Rapid model building
  • Excellent model explanations
  • Easy to use API
  • State-of-the-art model management and MLOps
  • Superb UI

Cons

  • Scenario building and What-if analysis
  • Add prescriptive analytics capability
  • Tighter integration with visualization tools
  • Classification Predictions
  • Time-Series Forecasts
  • Model Management
  • Prediction API
  • Demonstrable decrease in readmission rates (decreases are good)
  • Better census forecasting and financial planning
  • Improved patient care where it is need, improving patient ratings
We selected DataRobot for its "Automated" Machine Learning. Automation allows us to easily and quickly create machine learning models. The deployment process is simple, which was another key decision factor in choosing DataRobot over other platforms. We were pleasantly surprised by the Model Management functionality of DataRobot. Although Model Management (MM) was not a factor in our initial purchase, it was a factor in our renewal of the platform. I can't see us moving away from DataRobot unless the competitor had solid model management.

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

The DataRobot platform can create numeric predictions, binary and multi-class predictions, and time-series forecasts. Like any ML problem, DataRobot works best when your ML problem is well-framed and the dataset is formatted appropriately. DataRobot has built-in guardrails to prevent novice or inexperienced ML users from creating nonsensical models. The platform does a great job of presenting visibility into how the model determines a prediction. The explainability embedded in the platform makes it easy for non-data scientists to understand the models that were built.

DataRobot Feature Ratings

10.0
Connect to Multiple Data Sources
10
Extend Existing Data Sources
10
Automatic Data Format Detection
10
MDM Integration
Not Rated
8.5
Visualization
9
Interactive Data Analysis
8
9.0
Interactive Data Cleaning and Enrichment
7
Data Transformations
10
Data Encryption
Not Rated
Built-in Processors
10
9.8
Multiple Model Development Languages and Tools
9
Automated Machine Learning
10
Single platform for multiple model development
10
Self-Service Model Delivery
10
8.5
Flexible Model Publishing Options
9
Security, Governance, and Cost Controls
8

Evaluating DataRobot and Competitors

  • Price
  • Product Usability
Usability was our most important factor. Our team is comprised of data analysts, not data scientists. So we needed a platform that analysts, with a basic ML understanding, could use. It was clear in the POC phase that our team could spin up models quickly, deploy them, and score results.
Our evaluation of DataRobot included a 60 day POC. The DataRobot team met with us weekly to make sure we were on the right track. We chose a highly visible and relevant use case, so that helped gain traction with the executive team. I would change anything about our approach, it was nearly perfect.

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