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

Google Cloud Datalab 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)
Google Cloud Datalab
Ranking in Data Science Platforms
15th
Average Rating
7.8
Number of Reviews
6
Ranking in other categories
Data Visualization (19th)
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 Google Cloud Datalab is 1.0%, down from 1.5% 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.
Nilesh Gode - PeerSpot reviewer
Easy to setup, stable and easy to design data pipelines
The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.
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 feature is its robust statistical analysis capabilities."
"It offers very good visualization."
"SPSS is quite robust and quicker in terms of providing you the output."
"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"in terms of the simplicity, I think the SPSS basic can handle it."
"It has the ability to easily change any variable in our research."
"It is perfectly adequate if all you need are the results and not the trail of evidence."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"For me, it has been a stable product."
"All of the features of this product are quite good."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server."
"Visualisation, and the possibility of sharing functions are key features."
"The product's standout feature is a robust multi-file network with limited availability."
"The interface is very intuitive."
"It is a scalable solution…It is a pretty stable solution…The solution's initial setup process was pretty straightforward."
"I like being able to compare results across different training runs. The hyperparameter tuning function is a valuable feature because it provides the ability to run multiple experiments at the same time and compare results."
 

Cons

"The reports could be better."
"It could allow adding color to data models to make them easier to interpret."
"IBM SPSS Statistics does not keep you close to your data like KNIME."
"The statistics should be more self-explanatory with detailed automated reports."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"Better documentation on how to use macros."
"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."
"The technical support should be improved."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."
"The product must be made more user-friendly."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"The interface should be more user-friendly."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"Microsoft should also include more examples and tutorials for using this product.​"
"There's room for improvement in terms of binding the integration with Azure DevOps."
"I would like to see modules to handle Deep Learning frameworks."
"As for the areas for improvement in Microsoft Azure Machine Learning Studio, I've provided feedback to Microsoft. My company is a Gold Partner of Microsoft, so I provided my feedback in another forum. Right now, it is the number of algorithms available in the designer that has to be improved, though I'm sure Microsoft does it regularly. When you take a use case approach, Microsoft has done that in a lot of places, but not on the Microsoft Azure Machine Learning Studio designer. When I say use case basis, I meant recommending a product or recommending similar products, so if Microsoft can list out use cases and give me a template, it will save me a lot of time and a lot of work because I don't have to scratch my head on which algorithm is better, and I can go with what's recommended by Microsoft. I'm sure that isn't a big task for the Microsoft team who must have seen thousands of use cases already, so out of that experience if the team can come up with a standard template, I'm sure it'll help a lot of organizations cut down on the development time, as well as going with the best industry-standard algorithms rather than experimenting with mine. What I'd like to see in the next version of Microsoft Azure Machine Learning Studio, apart from the use case template, is the improvement of the availability of libraries. Microsoft should also upgrade the Python versions because the old version of Python is still supported and it takes time for Microsoft to upgrade the support for Python. The pace of upgrading Python versions of Microsoft Azure Machine Learning Studio and making those libraries available should be sped up or increased."
"Technical support could improve their turnaround time."
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"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."
 

Pricing and Cost Advice

"It's quite expensive, but they do a special deal for universities."
"More affordable training for new staff members."
"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."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"I rate the tool's pricing a five out of ten."
"The price of IBM SPSS Statistics could improve."
"We think that IBM SPSS is expensive for this function."
"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 pricing is quite reasonable, and I would give it a rating of four out of ten."
"The product is cheap."
"It is affordable for us because we have a limited number of users."
"We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees."
"There is a license required for this solution."
"ML Studio's pricing becomes a numbers game."
"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."
"There is a lack of certainty with the solution's pricing."
"The solution cost is high."
"The platform's price is low."
"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."
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
20%
Computer Software Company
14%
University
10%
Manufacturing Company
8%
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 Google Cloud Datalab?
Google Cloud Datalab is very customizable.
What needs improvement with Google Cloud Datalab?
Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a...
What is your primary use case for Google Cloud Datalab?
It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten serv...
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
No data available
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
Information Not Available
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Google Cloud Datalab vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.