Try our new research platform with insights from 80,000+ expert users

Cloudera Data Science Workbench vs Microsoft Azure Machine Learning Studio comparison

Sponsored
 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
Sponsored
Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Cloudera Data Science Workb...
Ranking in Data Science Platforms
21st
Average Rating
7.0
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in Data Science Platforms
3rd
Average Rating
7.8
Number of Reviews
57
Ranking in other categories
AI Development Platforms (2nd)
 

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.5%, down from 1.8% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 6.0%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
Enhancing survey analysis that provides valued insightfulness
I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.
Ismail Peer - PeerSpot reviewer
Useful for data science modeling but improvement is needed in MLOps and pricing
If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.
Klaus Lozie - PeerSpot reviewer
Provides good integration and used for data labeling
Lately, we have had some issues with the solution regarding labeling jobs. 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. Microsoft has a lot of documentation, but you can do it using the CLI, UI, or Python SDK version 2. You can have 100 ways of working, while I would like to have one way of working. It's very difficult to know what is best, according to Microsoft.

Quotes from Members

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

Pros

"The most valuable features are the small learning curve and its ability to hold a lot of data."
"It has the ability to easily change any variable in our research."
"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."
"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."
"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."
"It is perfectly adequate if all you need are the results and not the trail of evidence."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"The most valuable feature is its robust statistical analysis capabilities."
"The Cloudera Data Science Workbench is customizable and easy to use."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The UI is very user-friendly and that AI is easy to use."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The most valuable feature of the solution is the availability of ChatGPT in the solution."
"Azure's AutoML feature is probably better than the competition."
"It helps in building customized models, which are easy for clients to use​.​​"
"When you import the dataset you can see the data distribution easily with graphics and statistical measures."
"In terms of what I found most valuable in Microsoft Azure Machine Learning Studio, I especially love the designer because you can just drag and drop items there and apply the logic that's already available with the designer. I love that I can use the libraries in Microsoft Azure Machine Learning Studio, so I don't have to search for the algorithms and all the relevant libraries because I can see them directly on the designer just by dragging and dropping. Though there's a bit of work during data cleansing, that's normal and can't be avoided. At least it's easy to find the relevant algorithm, apply that algorithm to the data, then get the desired output through Microsoft Azure Machine Learning Studio. I also like the API feature of the solution which is readily available for me to expose the output to any consuming application, so that takes out a lot of headache. Otherwise, I have to have a developer who knows the API, and I have to have an API app, so all that is completely taken care of by the Microsoft Azure Machine Learning Studio designer. With the solution, I can concentrate on how to improve the data quality to get quality recommendations, so this lets me concentrate on my job rather than focusing on the regular development of APIs or the pipelines, in particular, the data pipelines pulling the data from other sources. All the data is taken care of and you can also concentrate on other required auxiliary activities rather than just concentrating on machine learning."
"The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem."
 

Cons

"It could provide even more in the way of automation as there are many opportunities."
"It could allow adding color to data models to make them easier to interpret."
"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."
"Needs more statistical modelling functions."
"The technical support should be improved."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"The solution needs to improve forecasting using time series analysis."
"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"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."
"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."
"In terms of improvement, I'd like to have more ability to construct and understand the detailed impact of the variables on the model. Their algorithms are very powerful and they explain overall the net contribution of each of the variables to the solution. In terms of being able to say to people "If you did this, you'll get this much more improvement" it wasn't great."
"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."
"Performance is very poor."
"In future releases, I would like to see better integration with Power BI within Microsoft Azure Machine Learning Studio."
"It would be nice if the product offered more accessibility in general."
"Integration with social media would be a valuable enhancement."
 

Pricing and Cost Advice

"We think that IBM SPSS is expensive for this function."
"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."
"More affordable training for new staff members."
"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."
"It's quite expensive, but they do a special deal for universities."
"I rate the tool's pricing a five out of ten."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
"The price of IBM SPSS Statistics could improve."
"The product is expensive."
"The platform's price is low."
"There is a lack of certainty with the solution's pricing."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"The solution operates on a pay-per-use model."
"The product's pricing is reasonable."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
"The solution cost is high."
"From a developer's perspective, I find the price of this solution high."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
816,406 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
University
9%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
35%
Manufacturing Company
11%
Healthcare Company
9%
Government
7%
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
10%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about IBM SPSS Statistics?
The software offers consistency across multiple research projects helping us with predictive analytics capabilities.
What is your experience regarding pricing and costs for IBM SPSS Statistics?
The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...
What needs improvement with IBM SPSS Statistics?
IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...
What do you like most about Cloudera Data Science Workbench?
I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don'...
What needs improvement with Cloudera Data Science Workbench?
The tool's MLOps is not good. It's pricing also needs to improve.
What is your primary use case for Cloudera Data Science Workbench?
We have different use cases. Our banking use case uses machine learning to identify customer life events and recommen...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
 

Also Known As

SPSS Statistics
CDSW
Azure Machine Learning, MS Azure Machine Learning Studio
 

Learn More

Video not available
 

Overview

 

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
IQVIA, Rush University Medical Center, Western Union
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Cloudera Data Science Workbench vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.