To improve DataRobot, I suggest enhancing model accuracy metrics and improving automation. The price points can also be improved. Another improvement that DataRobot needs is integrating the capability to modify the whole pipeline with Python.
Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.
Staff Specialist Data Scientist at a tech vendor with 5,001-10,000 employees
MSP
Top 10
Feb 12, 2025
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality.
Data Scientist at a tech services company with 10,001+ employees
Real User
Top 10
Jul 10, 2024
There are some performance issues when it comes to improvements. They also offer storage-related services compared to other tools like Admin, Azure, or AWS. It is easy to plug and play. Third-party tools for storage-related tasks are necessary, along with tools like DataRobot, which makes sourcing and destination data quite difficult. In terms of MLOps, they are not directly integrated with orchestration tools, and it would be beneficial if they integrated them. I've already given this feedback to their platform director.
Data Scientist at a tech services company with 11-50 employees
Real User
Dec 11, 2019
If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with simple integration, it would help us a lot.
DataRobot automates model building and deployment, simplifying MLOps with user-friendly interfaces. Its AutoML and feature engineering streamline model comparison, selection, and testing, enhancing efficiency and scalability.DataRobot facilitates efficient integration with cloud systems and data sources, reducing manual workload, enhancing productivity, and empowering data-driven decision-making. Its strengths lie in automating complex modeling tasks and supporting multiple predictive models...
To improve DataRobot, I suggest enhancing model accuracy metrics and improving automation. The price points can also be improved. Another improvement that DataRobot needs is integrating the capability to modify the whole pipeline with Python.
Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality.
There are some performance issues when it comes to improvements. They also offer storage-related services compared to other tools like Admin, Azure, or AWS. It is easy to plug and play. Third-party tools for storage-related tasks are necessary, along with tools like DataRobot, which makes sourcing and destination data quite difficult. In terms of MLOps, they are not directly integrated with orchestration tools, and it would be beneficial if they integrated them. I've already given this feedback to their platform director.
Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models.
If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with simple integration, it would help us a lot.