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.
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Automating model comparison speeds up development and reduces timelines
What is our primary use case?
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using DataRobot for two years.
What do I think about the stability of the solution?
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.
How are customer service and support?
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.
How would you rate customer service and support?
Positive
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
Highly automated solution allowing data scientists to build models easily
What is our primary use case?
We work on AI and ML use cases related to technology and IT.
What is most valuable?
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
What needs improvement?
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.
For how long have I used the solution?
I have been using DataRobot for three years. We are using V9.2 of the solution, although we have done a POC of V10.
What do I think about the stability of the solution?
The product is stable.
What do I think about the scalability of the solution?
We have 25 users using this solution.
How are customer service and support?
Support is very proactive in resolving the issues, and the response is too quick.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup is easy.
What about the implementation team?
I did the deployment.
Which other solutions did I evaluate?
We assessed multiple tools, however, we chose DataRobot specifically because of its AutoML features.
What other advice do I have?
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.
Which deployment model are you using for this solution?
On-premises
Easy to manage jobs and see the logs if there's any drift in a model, user-friendly, and the data munching is fast
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.
DataRobot review for modeling data science project
What do you like best about the product?
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.
What do you dislike about the product?
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
What problems is the product solving and how is that benefiting you?
Time saving in choosing the best model and data pre-processing.
Customer Lifetime Value Predictions
What do you like best about the product?
Automated feature engineering, multiple algorithms trained, automated comparisons
What do you dislike about the product?
Limited applications in probabilistic and neural network applications for Customer Lifetime Value prediction
What problems is the product solving and how is that benefiting you?
Forecasting and automated outputs
Algorithmia - Eliminating the dependency on dev ops
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
In addition to the bottleneck from code containerization, Algorithmia has also helped our team with knowledge transfer. Our organization is a large, global company and we have had difficulty in the past with sharing code across teams. Algorithmia has become a centralized platform for our teams to work.
It very easy to setup and run any AI model, low maintenance and good customer spport
What do you like best about the product?
Turn key solution to run any AI/NLP model.
1. Easy deployment and hassle free
2. Version management which will help to test any version.
3. GPU support
1. Easy deployment and hassle free
2. Version management which will help to test any version.
3. GPU support
What do you dislike about the product?
1. Cost is too high for statups
2. More documents
2. More documents
What problems is the product solving and how is that benefiting you?
1. Deployment problem.
2. Vertical scale
3. Maintenance
2. Vertical scale
3. Maintenance
Recommendations to others considering the product:
Turn key solution to deploy & run any AI/NLP model and hassle free.
Also able to develop automated algorithms for energy efficiency and saving applications
What do you like best about the product?
At Freiburger Energieoptimierung UG (www.frenop.de), DataRobot accelerated the pace of our analytics applied to energy efficiency and savings. Our team is integrated of Physicians who have a great knowledge of physical behaviors but we don’t have the time and the resources to develop a platform where we can analyze data from energy applications. DataRobot offers us automated solutions that are contributing for faster energy data analysis and also new discoveries with Machine Learning. The interface is very easy to use.
What do you dislike about the product?
DataRobot showed us that we need to have more data in order to proceed with some routines. Now we are investing more in time in finding this data in the real world so we can put it into these machine learning algorithms.
What problems is the product solving and how is that benefiting you?
It helping us with everything related to data visualization, data analytics, discovery of patterns, automation of behaviors, tracking of information, definition of different scenarios, controlling issues, performances, etc…
Great atomations
What do you like best about the product?
Some structures are very easy to proceed here. Specially for robotics applications.
What do you dislike about the product?
Nothing to complain at the moment. I am thankful to find good applications for the programed structures that the program offers.
What problems is the product solving and how is that benefiting you?
Automation of different interfaces that connect software with virtual reality, robotics and smart actors. We create solar panels that follow the sun!
Easy to Use and Functional
What do you like best about the product?
Ease of use for our IT organization and good support from the company.
What do you dislike about the product?
No downsides or major issues to complain about.
What problems is the product solving and how is that benefiting you?
We use this for predictive analytics in the cost mgmt space at my company. They provide great automated machine learning to provide cost avoidance opportunities to our users.
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