TrustRadius Insights for DataRobot are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Interface Excellence: Reviewers have consistently lauded the software for its excellent interface, emphasizing its speed, user-friendly development environment, and seamless facilitation of insights sharing with business users. Users find the intuitive design and smooth navigation crucial in enhancing their workflow efficiency and collaboration across teams.
Automodelling Capabilities: Many users have expressed satisfaction with the tool's automodelling capabilities, finding them more than sufficient for their needs. They appreciate the tool's impressive performance and explainability in generating models efficiently. The robust automation features streamline complex modeling tasks, allowing users to focus on interpreting results rather than intricate model building processes.
Support and Deployment Ease: Users highly value the platform's support services and expert technical assistance. They find deployment to be straightforward and stress-free, thanks to the excellent support provided by the platform. The reliable guidance from technical experts ensures a smooth implementation process, enabling users to leverage the platform effectively for their data analytics needs.
We use it to accelerate the building and deployment of our ML platform in our context to solve problems for financial applications.
Pros
ML training.
ML deployment.
Good documentation for implementing a first-API approach.
Cons
The MLOps platform is not great, especially the charts.
The license model presented to us didn't make full sense, you couldn't download any model using only the MLDev license.
Include more feature engineering options after uploading the data.
Likelihood to Recommend
It's very well suited, in my opinion, for small teams or those cases well your models are necessary to support internal business decisions. Probably is not the best tool if the product that you are selling really is an ML model; in that case, you can use it as a baseline, and then you can try to beat Datarobot results.
VU
Verified User
Engineer in Information Technology (Information Technology & Services company, 1001-5000 employees)
DataRobot helps us make sense of a large amount of information. Trying to predict what's going to happen is always difficult, but with DataRobot we're able to use the power of robots to do the heavy lifting for us. Let's face it — even if we could do all the complex math on our own, the time it would take would be completely prohibitive. DataRobot takes all that on, letting us get to the decision making instead of setting up models.
Pros
Time Series
Modeling
Awesome robot predictions
Cons
Flexibility of certain models
Time series included in general base package
Certain data source integration
Likelihood to Recommend
Time series data. Honestly, there's a lot of information that gets collected over time. Using DR, it becomes easier to track what's going on and get to the point where we're predicting what's going to happen. While DR is great at the big picture stuff, a lot of times (for quick/easy answers), it's better to just do the quick math yourself.
VU
Verified User
Director in Marketing (Marketing & Advertising company, 201-500 employees)
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.
VU
Verified User
Manager in Marketing (Information Technology & Services company, 10,001+ employees)
DataRobot supports prioritized AI/ML use cases, which range from predictive models around financial performance to employee-focused use cases.
Pros
Platform simple and intuitive.
Wonderful support.
Product roadmap well managed.
Cons
Socializing and promoting how their customers are using the platform and deriving value or making an impact.
Likelihood to Recommend
DataRobot is well suited when the business has really defined its use case well and you have internal SMNE's/resources to support the use cases. In addition, you need people with some data science and/or analytics knowledge. It is less suitable when the above scenarios don't exist.
We have a multi-classification problem and a relevancy problem we are using DataRobot to provide us a model for.
Pros
Extremely quick to provide prod ready models.
Super easy to maintain the models in a production environment.
Excellent audit trail
Cons
Offering a way within DataRobot to link multiple models as a single stack. Were using multiple binary classification models and we needed to link them through our own code. For us, it was easy since we were all programmers, to begin with, but for more business-oriented people, it would have been harder.
Likelihood to Recommend
DataRobot gave us models we could work on within a matter of days completely solving the pure machine learning part of the equation. People in upper management still have a hard time grasping how because of your tool we no longer need help with the AI part of things. I keep referring to your tool as a beast that needs to be fed period.
We use DataRobot for predictive modeling and to empower our Citizen Data Scientists.
Pros
Easy to use platform.
Easy to work on feature engineering and compare different types of algorithms on models.
Easy model deployment.
Cons
More training available.
Manageable data pipelines.
Likelihood to Recommend
DataRobot is perfect to train and deploy quick ML models to tackle down specific use cases. I believe DR should have annual conferences dedicated to users/developers but overall, DR is a great tool to use in the ML world.
DataRobots helps us to understand and use models in reality. Efficient in model configuration and selection. Great in documenting business models. We use it to clean large data and create data flow and automation. Prediction on continuous and categorical data. Connecting to data sources. Efficient in analyzing data and identifying indicators.
Pros
Automated model building.
Predicting APi's.
Hyperparameter tuning.
Crossfold validation.
Analyzing data.
Cons
The price.
More visibility into algorithms .
Likelihood to Recommend
The application is easy to use for data analysis. All of the screens have explanations of all data points, models thus making it easy to provide explanations. DataRobots helps with algorithms to analyze and decipher many machine learning techniques to provide models to assist companies in making the right decisions. To extend existing data sources.
We are using DataRobot to solve Marketing use cases, such as propensity to buy, ideal customer profiles, lead scoring, and behavioral purchasing trends for customer engagement. For Finance, we are using it to forecast revenue more accurately.
Pros
The interface is easy to use and self-service, with intuitive design.
The product is easy to use for data analysts and business users without having to be a data scientist, but if you are one the product is robust, so data scientists can also get some use.
All of the screens have explanations of all data points, models, and this makes it easy to provide explanations at the executive level and can easily modify models on the fly.
Cons
Not having any data cleansing or ETL capabilities, as most of the time you need to clean the data or append data before running models.
There are constraints to how many models can be run, so this is frustrating, but this is how the pricing model works.
It would be nice to have some me plug and play APIs for Salesforce and other tools instead of having to build your own.
Likelihood to Recommend
DataRobot is well suited for business analysts, data analysts, and data scientists. The platform explains its models and predictions in common language so that even executives and non-data analytical folks can understand.
