What is our primary use case?
I did a proof of concept (POC) at DISH Wireless (company name) before they were about to sign a contract. Currently, I'm working on another POC.
How has it helped my organization?
I used DataRobot extensively at DISH Wireless (my previous company). It's very user-friendly, and the data munching is fast. They have another product that helps with data processing as well. It connects well with Snowflake, which is pretty fast as an engine.
Different models, especially financial ones, run very fast in DataRobot. They have a strong focus on financial applications.
What is most valuable?
It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model. If there's drift, it's easier to look at the logs and retrain the model. So, it's got some really good features.
What needs improvement?
Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models.
For how long have I used the solution?
We have DataRobot now and we use it for some forecasting models, especially for financial metrics. On and off, I've used it for about four to five years.
What do I think about the stability of the solution?
It's pretty stable. Even if you're deploying to some sort of edge analytics, those things were also very convenient to do with DataRobot. It's definitely one of the top AI products I have seen so far.
What do I think about the scalability of the solution?
It's a very good product. It just depends on whether different clients want to use it because it comes with a cost.
Usually, in the first year, you get a big discount, and companies want to enroll. By the second year, they evaluate the cost. So, the cost is the most important factor. Otherwise, it's a really good tool.
How are customer service and support?
I had documents during the time when we ran the POC. We had data scientists from DataRobot and also executive salespeople from DataRobot who came and did a lot of one-on-one sessions with our team in Colorado, in Denver.
Which solution did I use previously and why did I switch?
I use Dataiku. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks.
Dataiku doesn't have that yet. They are also working on that area. But there are some key differences between the two products.
How was the initial setup?
I would rate my experience with the initial setup an eight out of ten, with ten being easy.
It is quite straightforward.
The deployment model is on-premises, in a private VPC.
Deployment did not take much time, depending on the project. It was pretty fast. We also used Databricks at the time, and compared to Databricks, DataRobot was very fast. During the POC, DataRobot outperformed every other product, like H2O and Dataiku. DataRobot stood out in our POC the most.
What about the implementation team?
The deployment was done by an internal team. We internally ran a POC. After that, we decided to go with DataRobot and Dataiku, both companies at the time. Different teams use DataRobot differently.
What other advice do I have?
The solution itself is definitely nine out of ten. It's a really good solution. If cost is not an issue for most companies, they would love to have DataRobot. That's how most of the clients have been.
*Disclosure: My company does not have a business relationship with this vendor other than being a customer.
