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Darwin vs Microsoft Azure Machine Learning Studio comparison

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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)
Darwin
Ranking in Data Science Platforms
27th
Average Rating
8.0
Number of Reviews
8
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 Darwin is 0.3%, up from 0.3% 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
Sep 21, 2023
Enhancing survey analysis that provides valued insightfulness
I use it to analyze questionnaire surveys related to a product, solution, or application, such as open data services, which I provide to consumers and end-users. These surveys contain evaluation assessments, and I use SPSS to analyze the responses The most valuable feature is its robust…
AC
Jun 11, 2021
Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.
There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.
Klaus Lozie - PeerSpot reviewer
Apr 22, 2024
Provides good integration and used for data labeling
We use Microsoft Azure Machine Learning Studio to train our models and for data labeling The solution's most beneficial feature is its integration with Azure. We are an Azure-based company, and the solution's integration feature allows us to log in through Cosmos DB or Application Insights.…

Quotes from Members

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

Pros

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."
"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."
"SPSS is quite robust and quicker in terms of providing you the output."
"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."
"The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"It is perfectly adequate if all you need are the results and not the trail of evidence."
"The most valuable features are the small learning curve and its ability to hold a lot of data."
"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."
"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."
"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."
"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"The thing that I find most valuable is the ability to clean the data."
"The solution is very fast and simple for a data science solution."
"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."
"Auto email and studio are great features."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"​It has helped in reducing the time involved for coding using R and/or Python."
"Azure's AutoML feature is probably better than the competition."
"The product's initial setup phase is easy."
 

Cons

"One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated."
"There is a learning curve; it's not very steep, but there is one."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"The solution needs to improve forecasting using time series analysis."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
"I think the visualization and charting should be changed and made easier and more effective."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."
"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."
"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."
"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."
"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."
"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."
"The analyze function takes a lot of time."
"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."
"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."
"The interface is a bit overloaded."
"Using the solution requires some specific learning which can take some time."
"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."
"There should be data access security, a role level security. Right now, they don't offer this."
"The speed of deployment should be faster, as should testing."
"The price could be improved."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
 

Pricing and Cost Advice

"It's quite expensive, but they do a special deal for universities."
"We think that IBM SPSS is expensive for this function."
"I rate the tool's pricing a five out of ten."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"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."
"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."
"The price of IBM SPSS Statistics could improve."
"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."
"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."
"I believe our cost is $1,000 per month."
"As far as I understand, my company is not paying anything to use the product."
"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."
"The product's pricing is reasonable."
"ML Studio's pricing becomes a numbers game."
"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."
"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."
"The solution cost is high."
"In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."
"My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
University
10%
Computer Software Company
9%
Manufacturing Company
8%
Computer Software Company
24%
Government
12%
Real Estate/Law Firm
12%
Educational Organization
11%
Financial Services Firm
12%
Computer Software Company
11%
Manufacturing Company
10%
Healthcare Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
While the pricing of the product may be higher, the accompanying service and features justify the investment. However...
What needs improvement with IBM SPSS Statistics?
In some cases, the product takes time to load a large dataset. They could improve this particular area.
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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

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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
Hunt Oil, Hitachi High-Tech Solutions
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Darwin vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: October 2024.
814,763 professionals have used our research since 2012.