DataRobot Information Reviews & Insights

Score8.3 out of 10

87 Reviews and Ratings

Community insights

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.

Reviews

2 Reviews

Information

Great product for the AI team, multiplies our productivity and value generation

Rating: 10 out of 10

Incentivized

March 23, 2022

Use Cases and Deployment Scope

We use DataRobot to prototype new models that might solve business problems with the data that we have in our data warehouse. The models that have a positive return and fit in our business process are then evaluated and documented, and presented to a commission that approves them. Once they are approved, we also use DataRobot to deploy those models in production and monitor and govern the model in production, measuring the value that it is generating through time.

Pros

Quickly solving data science problems
Monitoring models in production
Compare different approaches in solving a problem with AI

Cons

Integrating the capability to modify with python the whole pipeline

Likelihood to Recommend

It is especially best suited for companies with many AI use cases due to its rapid prototyping. It is also beneficial when the data science team is not big since it can multiply the productivity of the team, allowing each data scientist to work on more than one project at the same time, leveraging the platform speed. Also good when the data scientists are not experts and can benefit from the guardrails that the platform offers.

Ignacio Vilaplana

Lead Data Scientist in Information Technology at Euskaltel (Telecommunications, 1001-5000 employees)

Vetted Review

Reseller

3 years of experience

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DataRobot makes us use and understand models in real business easily

Rating: 10 out of 10

Incentivized

December 9, 2021

Use Cases and Deployment Scope

We solve different kinds of business problems in the financial and retail industries. We are very satisfied with DataRobot´s capabilities: data prep of large volume of data, model creation and evaluation and deployment and monitoring . We solve use cases like collection, risk, audit, scoring, demand prediction, next best offer, price optimizing. We help analysts and data scientists from large banks and retailers.

Pros

Data prep: cleaning large data, creation of data flows and automation
Model configuration and selection
Deployment and monitoring models
Documenting and explaining the "why" of the models so understanding and explaining to business is made easy.

Cons

Data Scientist facing customers DSFC. They are very good but I think there should be more or include start-up services to clients more accessible.
Integration of the different tools. DataRobot had acquired several companies and integrating them should not be noted.
Automatic data mining
More algorithms and facilities for anomaly detection.

Likelihood to Recommend

Best suited: You need to get models in production fast and many of them. We have an experience where data scientists in a company spend more than two months cleaning and trying to create models. We created models and put them into production in less than a week. Less suited: Marketing scenarios where you need segmentation done automatically but made visible and controllable to data scientists and marketers.

Iván Pech Escalante

Director in Professional Services at Datateam Consulting (Computer Software, 11-50 employees)

Vetted Review

Reseller

2 years of experience

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