Compare Databricks vs. Microsoft Azure Machine Learning Studio

Databricks is ranked 4th in Data Science Platforms with 9 reviews while Microsoft Azure Machine Learning Studio is ranked 7th in Data Science Platforms with 9 reviews. Databricks is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.4. The top reviewer of Databricks writes "Has a good feature set but it needs samples and templates to help invite users to see results". 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, Microsoft Azure Machine Learning Studio and Cloudera Data Science Workbench, whereas Microsoft Azure Machine Learning Studio is most compared with Databricks, Alteryx and Amazon SageMaker. See our Databricks vs. Microsoft Azure Machine Learning Studio report.
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Most Helpful Review
Find out what your peers are saying about Databricks vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
396,515 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
Imageflow is a visual tool that helps make it easier for business people to understand complex workflows.I haven't heard about any major stability issues. At this time I feel like it's stable.The time travel feature is the solution's most valuable aspect.I work in the data science field and I found Databricks to be very useful.The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great.Automation with Databricks is very easy when using the API.We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search

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The UI is very user-friendly and that AI is easy to use.The most valuable feature is data normalization.The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses.The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure.Visualisation, and the possibility of sharing functions are key features.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.When you import the dataset you can see the data distribution easily with graphics and statistical measures.Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.

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Cons
The product needs samples and templates to help invite users to see results and understand what the product can do.Pricing is one of the things that could be improved.Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with.It would be very helpful if Databricks could integrate with platforms in addition to Azure.The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets.It should have more compatible and more advanced visualization and machine learning libraries.Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems.The integration features could be more interesting, more involved.

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When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers.The data cleaning functionality is something that could be better and needs to be improved.Integration with social media would be a valuable enhancement.If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice.Operability with R could be improved.I would like to see modules to handle Deep Learning frameworks.I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated.Enable creating ensemble models easier, adding more machine learning algorithms.

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Pricing and Cost Advice
I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful.Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.

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From a developer's perspective, I find the price of this solution high.When we got our first models and were ready for the user acceptance testing, our licensing fees were between €2,500 ($2,750 USD) and €3,000 ($3,300 USD) monthly.To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS.

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Ranking
4th
Views
9,441
Comparisons
8,518
Reviews
6
Average Words per Review
555
Avg. Rating
8.7
7th
Views
10,077
Comparisons
8,352
Reviews
8
Average Words per Review
479
Avg. Rating
7.3
Top Comparisons
Compared 18% of the time.
Also Known As
Databricks Unified Analytics, Databricks Unified Analytics PlatformAzure Machine Learning
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Databricks
Microsoft
Overview

Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

Offer
Learn more about Databricks
Learn more about Microsoft Azure Machine Learning Studio
Sample Customers
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
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Top Industries
VISITORS READING REVIEWS
Software R&D Company42%
Media Company9%
Comms Service Provider8%
Government7%
VISITORS READING REVIEWS
Software R&D Company30%
Comms Service Provider18%
Media Company6%
Manufacturing Company5%
Find out what your peers are saying about Databricks vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: January 2020.
396,515 professionals have used our research since 2012.
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.