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 has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"The solution is very easy to use."
"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."
"The processing capacity is tremendous in the database."
"The solution offers a free community version."
"ML Studio is very easy to maintain."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it."
"The product's standout feature is a robust multi-file network with limited availability."
"The solution is scalable."
"The solution's most beneficial feature is its integration with Azure."
"The solution is really scalable."
"It's a great option if you are fairly new and don't want to write too much code."
"The product should provide more advanced features in future releases."
"There are no direct connectors — they are very limited."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"Anyone who doesn't know SQL may find the product difficult to work with."
"It would be nice to have more guidance on integrations with ETLs and other data quality tools."
"I have seen better user interfaces, so that is something that can be improved."
"Operability with R could be improved."
"There should be data access security, a role level security. Right now, they don't offer this."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The interface is a bit overloaded."
"We can create a label job, but we still have to use the Azure Machine Learning REST APIs, which are not yet supported in the Python SDK version 2."
"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."
"It is not easy. It is a complex solution. It takes some time to get exposed to all the concepts. We're trying to have a CI/CD pipeline to deploy a machine learning model using negative actions. It was not easy. The components that we're using might have something to do with this."
"I would like to see modules to handle Deep Learning frameworks."
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 51 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, 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. See our Databricks vs. Microsoft Azure Machine Learning Studio report.
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