DataRobot Enterprise AI Suite for AWS

My main use case for DataRobot  is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of the examples is I use DataRobot  to predict which students are likely to accept the university offer. It basically helps us and the admission team to focus their efforts more efficiently. It also helps us with data matching and cleaning in large data sets, which reduces manual work.

The prediction helps our team and the admission team to prioritize outreach to the students who are most likely to accept the offer. They inform marketing and follow-up strategies as well, making efforts more efficient and quicker. One example is if DataRobot predicts a student has a high likelihood of accepting, the team can send personalized emails or call them to provide guidance and support directly to these students. It basically focuses on these specific students which have been just highlighted by DataRobot. It also reduces time spent on students who are unlikely to enroll, allowing us to use our resources more efficiently only on the people who we think are actually going to come back and enroll with us.

I do use DataRobot for many other things as well. For example, other than the target of student enrollment, I use DataRobot for data cleaning. I do the cleaning of deduplication as well. I also use this to detect any anomalies. It basically helps me to automate all the repetitive tasks and saves me some time. One example I can share is I use it to flag duplicate student records across multiple systems, which used to take us hours to do before, and now it's done a lot more quickly by using DataRobot.

There are many features that I appreciate about DataRobot. Some of the features which I personally prefer are the ones that save time. First, I would start with the automating features. If I want to do the data preparation, clean the raw data, or upload a student admission data, DataRobot automatically generates features such as the number of applications in the last month and previous offers accepted, and it can remove duplicates as well. This is one of my favorite features. Secondly, it tests my machine learning models for me, and the testing and selection are very efficient. For example, if I want to run many algorithms, DataRobot will compare them and pick the best one, saving me time from manually checking which one is the best. Lastly, another feature that I appreciate is the integration and scalability with our cloud system. It helps us connect with the various data sources we work with in our university, such as SITS, Azure  SQL, and CSV exports, allowing DataRobot to handle joins and feature engineering effectively without requiring extensive coding from me.

DataRobot has positively impacted our organization in many ways. First, it has improved efficiency; tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours. This ultimately helps us make better decisions, particularly with admission data where we can rely heavily on the predictions made by DataRobot. It has also helped to reduce a lot of manual work and has allowed me to execute automation tasks more quickly. Furthermore, DataRobot provides scalable analytics, enabling us to run multiple predictive models across different departments without needing extra staff or extensive infrastructure. For instance, it allows the admission team to prioritize outreach to students likely to apply, ensuring we spend our resources effectively.

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.

I have been using DataRobot for about one year, which is about the past 12 months.

DataRobot's customer support is good but could improve with quicker response times and better documentation or community support. The scalability is robust, managing large data sets, although it sometimes slows down when processing bigger data, but being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.

Neutral

Previously, we used different solutions, including manual model building through Python, Excel, and Azure  ML for some projects. We switched due to the burdensome manual workflows that were time-consuming and required extensive coding, making it difficult to test multiple models quickly. DataRobot allowed us to experiment faster, achieve better model accuracy, and facilitate simpler collaboration without needing high-level programming skills.

We have indeed seen a return on investment. On average, we're saving about 10 to 15 hours per project. The efficiency has greatly improved; tasks that used to take a day now take mere hours. While we haven't reduced staff, their workload has lightened, enabling them to accomplish more within the same timeframe. The standout metric remains the 10 to 15 hours saved per project.

While pricing falls more under my IT colleagues, from my perspective, the overall experience feels justified. The premium pricing is reasonable for the value provided, and I'd say it's worth the investment. The setup cost was minimal because it's cloud-hosted, eliminating the need for heavy on-premises infrastructure, allowing us to start using it immediately after purchase.

We evaluated several options, including Azure Machine Learning  and manual Python workflows. DataRobot offered the best combination of automation, model accuracy, and ease of use, which ultimately saved us significant time and resources, making it the clear choice.

For those looking into DataRobot, I recommend starting with a small project to grasp the workflow before scaling. Utilizing the automations offered and dedicating some time for training is key, along with collaborating and sharing models and dashboards within the team to maximize the platform's value.

DataRobot is a powerful, user-friendly platform that saves time and provides accuracy, although improvements are needed in handling larger data sets and flexibility. You can use my real name for the public review. I have provided this review with a rating of 7.

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.