Azure Data Science Virtual Machines (DSVM) vs. IBM SPSS Modeler

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Azure Data Science Virtual Machines (DSVM)
Score 8.4 out of 10
N/A
Available on Microsoft's Azure platform, Data Science Virtual Machines (DSVMs) are comprehensive pre-configured virtual machines for data science modelling, development and deployment.N/A
IBM SPSS Modeler
Score 7.1 out of 10
N/A
IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
$4,670
per year
Pricing
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Editions & Modules
No answers on this topic
IBM SPSS Modeler Personal
4,670
per year
IBM SPSS Modeler Professional
7,000
per year
IBM SPSS Modeler Premium
11,600
per year
IBM SPSS Modeler Gold
contact IBM
per year
Offerings
Pricing Offerings
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsIBM SPSS Modeler Personal enables users to design and build predictive models right from the desktop. IBM SPSS Modeler Professional extends SPSS Modeler Personal with enterprise-scale in-database mining, SQL pushback, collaboration and deployment, champion/challenger, A/B testing, and more. IBM SPSS Modeler Premium extends SPSS Modeler Professional by including unstructured data analysis with integrated, natural language text and entity and social network analytics. IBM SPSS Modeler Gold extends SPSS Modeler Premium with the ability to build and deploy predictive models directly into the business process to aid in decision making. This is achieved with Decision Management which combines predictive analytics with rules, scoring, and optimization to deliver recommended actions at the point of impact.
More Pricing Information
Community Pulse
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Features
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Azure Data Science Virtual Machines (DSVM)
8.7
Ratings
4% above category average
IBM SPSS Modeler
7.0
Ratings
18% below category average
Connect to Multiple Data Sources7.80 Ratings7.00 Ratings
Extend Existing Data Sources9.00 Ratings7.00 Ratings
Automatic Data Format Detection9.00 Ratings00 Ratings
MDM Integration9.00 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Azure Data Science Virtual Machines (DSVM)
8.1
Ratings
3% below category average
IBM SPSS Modeler
-
Ratings
Visualization7.80 Ratings00 Ratings
Interactive Data Analysis8.40 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Azure Data Science Virtual Machines (DSVM)
8.9
Ratings
9% above category average
IBM SPSS Modeler
-
Ratings
Interactive Data Cleaning and Enrichment9.00 Ratings00 Ratings
Data Transformations9.00 Ratings00 Ratings
Data Encryption9.00 Ratings00 Ratings
Built-in Processors8.40 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Azure Data Science Virtual Machines (DSVM)
8.4
Ratings
0% above category average
IBM SPSS Modeler
-
Ratings
Multiple Model Development Languages and Tools8.40 Ratings00 Ratings
Automated Machine Learning9.00 Ratings00 Ratings
Single platform for multiple model development7.80 Ratings00 Ratings
Self-Service Model Delivery8.40 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Azure Data Science Virtual Machines (DSVM)
7.7
Ratings
10% below category average
IBM SPSS Modeler
-
Ratings
Flexible Model Publishing Options8.40 Ratings00 Ratings
Security, Governance, and Cost Controls7.00 Ratings00 Ratings
User Ratings
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Likelihood to Recommend
8.4
(0 ratings)
7.0
(0 ratings)
Usability
-
(0 ratings)
8.0
(0 ratings)
Support Rating
-
(0 ratings)
10.0
(0 ratings)
User Testimonials
Azure Data Science Virtual Machines (DSVM)IBM SPSS Modeler
Likelihood to Recommend
To leverage a high processing workload that can be done fast instead of in multiple days or hours.
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Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.
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Pros
  • Leveraging data.
  • Computer vision.
  • Data science.
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  • A very nice and easy to use interface.
  • A great variety of analytics, from statistical calculation to data validation and predictive statistics.
  • Has a steep learning curve.
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Cons
  • Azure DSVM pricing must be reduced so that an AI-based start-up can use the Azure DSVM.
  • Azure must create an environment to use Azure DSVM offline as well.
  • Lack of frameworks
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  • Some Analyses aren't there out of the box but can be added through open languages like R and Python.
  • Graphs could be better.
  • Unable to read data stored in OLAP databases
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Usability
No answers on this topic
The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources
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Support Rating
No answers on this topic
The online support board is helpful and the free add ons are incredibly appreciated.
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Alternatives Considered
Azure DSVM provides [many] cost-effective solutions rather than using the Amazon SageMaker. Amazon products are a little more detailed products but this detailing is [a] little costly in comparison to the Azure. Azure DSVM is way more controlled than the Amazon SageMaker and it is very cost-effective as compared to Amazon SageMaker. We are already managing Aure services so we explored the Azure DSVM which turned out [to] be a good choice.
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We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.
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Return on Investment
  • Azure DSVM is little costly with long term support for ML based environments.
  • Azure DSVM is very good for short tasking and costs us [a] little low than the on-prem server.
  • [Scaling] option is very convenient.
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  • I am able to study and work from home sustainably
  • I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
  • Market research for my business
  • Help other small businesses to create viable and high quality products and services
  • Contribute to research projects: ethical, high quality data analyses and interpretation
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ScreenShots

IBM SPSS Modeler Screenshots

Screenshot of Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.Screenshot of Explore geographic data, such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.Screenshot of Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails and social media comments.Screenshot of Use R, Python, Spark, Hadoop and other open source technologies to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you keep control.