DataRobot Reviews & Product Details

I like that DataRobot makes it easier to build and manage machine learning models without needing to do everything manually and saving weeks of manual coding and guesswork. Review collected by and hosted on G2.com.

Team members who aren't data experts often struggle to understand how it makes decisions. Review collected by and hosted on G2.com.

I find DataRobot really helpful for efficiently managing AI agents. It impresses me with its ability to handle multiple agents simultaneously, which is a big plus. I also appreciate its capability to manage AI agent tasks, create models, do coding, make command files, and create AI agents seamlessly. The initial setup was easy, which made getting started less stressful. Review collected by and hosted on G2.com.

I find the high cost of licensing fees to be a bit of a drawback with DataRobot. It could be more budget-friendly. Review collected by and hosted on G2.com.

I like Data Robot because it makes the process of building and managing AI models easier. The platform is user-friendly, saves time, and provides useful tools for analyzing and deploying models. Review collected by and hosted on G2.com.

The platform can take some time to learn for new users, especially when working with advanced features. Some options may also feel a little complex at first. Review collected by and hosted on G2.com.

I like DataRobot because it makes machine learning simple and saves a lot of time through automation. Review collected by and hosted on G2.com.

The pricing can be a bit high and some advanced features take time to learn. Review collected by and hosted on G2.com.

So when it helped me a lot in processing huge data and it help in get results with deployment and forecast in quarter of the time I used to spend before Review collected by and hosted on G2.com.

While working in the start for me some terminology was really confusing but my senior explained me our specific used case it got a lot easier Review collected by and hosted on G2.com.

What I like best about DataRobot is its automated machine learning capabilities, which make it easy to build, deploy, and manage predictive models quickly without needing deep technical expertise. Review collected by and hosted on G2.com.

What I dislike about DataRobot is that it can be quite expensive and may feel less flexible for highly customized modeling compared to open-source solutions. Review collected by and hosted on G2.com.

The interface is pretty easy to navigate. It also makes machine learning very accessible cutting down a lot of manual work. Review collected by and hosted on G2.com.

There honestly doesn’t seem much to dislike but sometimes the number of features can feel a bit overwhelming. The learning curve takes some time. Review collected by and hosted on G2.com.

It basically helps in deploying and modelling ML models. It is automated technology that helps us in data preprocessing, feature engineering choosing the best model and even hyperparameter tuning. It is time-saving and can handle large data. and always choose best model for our project. Review collected by and hosted on G2.com.

it is costly and not as good as IBM Watson. Sometime it does not gives us the best model and we have to choose ourselves and also customization are limited Review collected by and hosted on G2.com.

Automated feature engineering, multiple algorithms trained, automated comparisons Review collected by and hosted on G2.com.

Limited applications in probabilistic and neural network applications for Customer Lifetime Value prediction Review collected by and hosted on G2.com.

Algorithmia has really put the power back in the hands of our data scientists and R&D developers. One of the key bottlenecks our organization faced was pushing code from dev into production, due to the time spent containerizing our code and the skillset required to do so. Especially in situations when we are constantly iterating and publishing new versions, the ability to containerize without support from dev ops is extremely powerful. Review collected by and hosted on G2.com.

Two of the main areas for improvement would be parity with Azure repos and more business-focused reporting of platform consumption. We have stakeholders in both technical and business areas, and struggle to communicate this across teams. Additionally, SAS is a very popular language in our line of business, which Algorithmia does not currently support. Review collected by and hosted on G2.com.