We performed a comparison between DataRobot and Microsoft Azure Machine Learning Studio based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."DataRobot can be easy to use."
"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."
"The most valuable feature is data normalization."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"The initial setup is very simple and straightforward."
"One of the notable advantages is that it offers both a visual designer, which is user-friendly, and an advanced coding option."
"ML Studio is very easy to maintain."
"The solution is easy to use and has good automation capabilities in conjunction with Azure DevOps."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
"It's easy to deploy."
"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."
"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"The solution's initial setup process is complicated."
"The solution should be more customizable. There should be more algorithms."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"Technical support could improve their turnaround time."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"The interface is a bit overloaded."
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DataRobot is ranked 13th in AI Development Platforms while Microsoft Azure Machine Learning Studio is ranked 1st in AI Development Platforms with 51 reviews. DataRobot is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of DataRobot writes "Easy to use, priced well, and can be customized". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". DataRobot is most compared with Amazon SageMaker, RapidMiner, Datadog, Alteryx and SAS Predictive Analytics, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and Google Cloud AI Platform. See our DataRobot vs. Microsoft Azure Machine Learning Studio report.
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