DataRobot Enterprise AI Suite for AWS

In our day-to-day use, I utilize DataRobot to speed up our development process through its GUI capability. Once I set up our connection with a back-end data set, whatever the project I work on next, it automatically integrates the data and catalogs. I can continue with feature engineering, prediction modeling, and deployment all in one place. I bring in data and train models using GUI and API methods.

DataRobot is equipped with a GUI-based approach that simplifies the process of feature engineering and model training. It provides AutoML capabilities, which allow for comparing thousands of models and selecting the best-suited one based on business requirements. By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.

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.

I have been using DataRobot for two years.

Personally, I haven't experienced any significant issues with stability, but there were some enhancement features added, like resolving issues with duplicating projects in newer versions. Overall, it seems to be a very stable product.

I have regular meetings with DataRobot's support team as part of our licensing model, and they provide excellent service. They answer all my questions and share guidance on using DataRobot scripts if certain functionalities are not available in the UI.

Positive

I would recommend DataRobot because if there is something not included in the UI, I have the freedom to use its Python API, which extends the capability for different use cases. Additionally, I would rate DataRobot eight out of ten for my overall experience.

We work on AI and ML use cases related to technology and IT.

DataRobot  is highly automated, allowing data scientists to build models easily. It excels in MLOps, offering robust solutions. Its platform integrates end-to-end processes, simplifying model building and deployment, which addresses many pain points for data scientists. It has very easy access, and they made everything into a single platform

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.

I have been using DataRobot for three years. We are using V9.2 of the solution, although we have done a POC of V10.

The product is stable.

We have 25 users using this solution.

Support is very proactive in resolving the issues, and the response is too quick.

Positive

The initial setup is easy.

I did the deployment.

We assessed multiple tools, however, we chose DataRobot specifically because of its AutoML features.

Based on your similar requirements, V10.0 has very cool features related to AI Generation. I would suggest the team, too. This is very good for data scientists or people who don't want to code.

Even the documentation is well-maintained in terms of its capabilities. It's easy to navigate. The support documents are good. Even someone with basic IT knowledge can easily navigate. If you're an IT engineer, you can efficiently perform operations using it.

We have deployed eight to nine use cases on DataRobot and have seen a tremendous response in accuracy and performance. We are pleased because we conducted a comparison. We took a model we built using a sample Python on a local machine and applied the same data and process using DataRobot Autopilot. The results were pretty amazing, with promising accuracy and recall.

The accessibility is so easy. Even a college graduate with essential experience can use it. Suppose I do the same model in Databricks  and want to monitor my MLOps pipeline. So, I need to use a third-party framework again, like MLflow, Kubeflow, Airflow , or whatever. I need to build my dashboards and everything, customization dashboards. However, everything is available in DataRobot. I can use it directly. They have a new option called DataRobot apps. So, on the predictions, we can even create customized apps. I can build my dashboard, and I can develop my applications.

Overall, I rate the solution an eight out of ten.

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.

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.

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.

Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models.

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.

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.

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.

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.

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.

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.

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.

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.