We performed a comparison between Databrick and Microsoft Azure Machine Learning Studio based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison of Results: Based on the parameters we compared, Microsoft Azure Machine Learning Studio seems to be a slightly superior solution. All other things being more or less equal, our reviewers found Databricks rather expensive to purchase. Some users also feel that Microsoft Azure Machine Learning Studio has better machine learning capabilities.
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."
"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."
"The solution is very easy to use."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"We have the ability to scale, collaborate and do machine learning."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
"It's easy to deploy."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"It helps in building customized models, which are easy for clients to use."
"The UI is very user-friendly and that AI is easy to use."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"Azure's AutoML feature is probably better than the competition."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"Databricks' technical support takes a while to respond and could be improved."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"It would be great if Databricks could integrate all the cloud platforms."
"The platform's integration feature could be better."
"One area where Azure Machine Learning Studio could improve is its user interface structure."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"The price of the solution has room for improvement."
"Operability with R could be improved."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"It would be great if the solution integrated Microsoft Copilot, its AI helper."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 50 reviews. Databricks is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". 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". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Dremio and Azure Stream Analytics, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Azure OpenAI, TensorFlow, Google Cloud AI Platform and Dataiku Data Science Studio. See our Databricks vs. Microsoft Azure Machine Learning Studio report.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.