The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…
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Streamlit
Score 8.9 out of 10
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Streamlit is an open-source Python library designed to make it easy to build custom web-apps for machine learning and data science, from the company of the same name in San Francisco. Streamlit also hosts its community's Streamlit Component offered via API to help users get started.
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the …
DataRobot outperforms SPSS in terms of speed and efficiency. While I continue to rely on SPSS for tasks like data cleanup and data engineering, I have noticed that DataRobot significantly excels when it comes to building models. Its speed and user-friendly interface make it the …
Comparable to H2O but my company chose DataRobot so that's why I'm using it. Pricing is reasonable and the feature coverage is probably better from an end-to-end perspective. DataRobot has less flexibility than Amazon SageMaker but is a lot simpler to use, which again for a …
Alteryx is more of data processing only with user-friendly interface for non-technical users. Data Robot is more than that and can provide intelligent models for machine learning.
I have not used any comparable products. Compared to using commonly available open source libraries for machine learning, DataRobot automatically manages the partition of data, pre-processing of data, construction of processing pipelines and the evaluation of models on an …
Robots vs. Robots. It was necessarily me who selected DR instead, but having used both, I find that DR is better suited to our needs and is just more accessible. You don't need to be a complete expert in the field to be able to use DR's platform, more just being able to …
When we ran the purchase process, two factors were critical: price of course and the customer success service as we were new in this datascience world. H2O and DataRobot were the finalists (Dataiku too expensive for our needs), but we decide to choose DataRobot as they give us …
DataRobot provided the perfect balance of features and price points. The other tools we tried were very expensive and provided extra things that we really didn't need. Some of the other tools also required you to host them on a server at your institution or pay for their cloud …
We consistently return to DataRobot for its ease of use and ability to get the job done without major hurdles. Thus far, we just haven't found that in other products. H2O.ai (Driverless AI): several test models did not complete, and H2O.ai team could not explain why. Sagemaker:…
We've just had an intro but DataRobot is much more specialized in predictive analytics. Dataiku seems for me a platform that aims to cover a little bit all the steps or processes of a D&A team and with this approach, you may be doing a trade-off in quality and power
DataRobot is the product that seemed to have the most professional platform all in all. It was also the best one for the second part of the model development, which is monitoring what the model is doing in production and governing what that model was doing, giving us the …
I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take …
DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.
- Don't want to pay Tableau $1,000 / seat? Use Streamlit - Want fully custom views and navigation? Use Streamlit - Want access to Machine Learning and not just your dev team? Use Streamlit - Want to keep things internal and secure? Use Streamlit - Want your Data Science team to be able to crank out projects quickly? Use Streamlit - Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.
Further improvements to their text analysis tool, to be more like the Qualtrics text analysis tool, would be a great addition. Qualtrics has templates built into their text analysis tool for customer service, quality control, etc, and will automatically slot your text responses into categories associated with certain sub areas of those larger categories.
Recent Security issues (they quickly released an update to combat this though...)
Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast.
DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.
As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.
I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.