Compare Databricks vs. Microsoft Azure Machine Learning Studio

Databricks is ranked 5th in Data Science Platforms with 1 review while Microsoft Azure Machine Learning Studio is ranked 4th in Data Science Platforms with 6 reviews. Databricks is rated 10.0, while Microsoft Azure Machine Learning Studio is rated 7.4. The top reviewer of Databricks writes "Good build-in optimization, easy to use with a good user interface". 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, Amazon SageMaker and Alteryx.
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Most Helpful Review
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Quotes From Members

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Pros
The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly.

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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.MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.The graphical nature of the output makes it very easy to create PowerPoint reports as well.Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently.

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Cons
The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment.

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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.​It could use to add some more features in data transformation, time series and the text analytics section.Microsoft should also include more examples and tutorials for using this product.​

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Pricing and Cost Advice
Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.

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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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Comparisons
6,780
Reviews
1
Average Words per Review
771
Avg. Rating
10.0
4th
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9,085
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5
Average Words per Review
353
Avg. Rating
7.4
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 Company39%
Media Company10%
Comms Service Provider7%
Government7%
VISITORS READING REVIEWS
Software R&D Company29%
Comms Service Provider19%
Manufacturing Company6%
Media Company5%
Find out what your peers are saying about Alteryx, Knime, IBM and others in Data Science Platforms. Updated: October 2019.
378,809 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.
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