IBM SPSS Modeler vs. MLReef

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM SPSS Modeler
Score 7.8 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.
$499
per month
MLReef
Score 9.1 out of 10
Enterprise companies (1,001+ employees)
MLReef is a Machine Learning development platform that aims to democratize ML innovation across the entire organization. Distributed ML Development: - up to 5X in ML development throughput - up to 85% less dependency on internal data science capacity - Distributed workload on complex data tasks with seamless involvable domain experts - Higher acceptance of deploye models ad development is a joint task Q: What is Distributed ML…N/A
Pricing
IBM SPSS ModelerMLReef
Editions & Modules
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
No answers on this topic
Offerings
Pricing Offerings
IBM SPSS ModelerMLReef
Free Trial
YesYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesYes
Entry-level Setup FeeOptionalOptional
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
IBM SPSS ModelerMLReef
Top Pros
Top Cons
Best Alternatives
IBM SPSS ModelerMLReef
Small Businesses
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Mathematica
Mathematica
Score 8.2 out of 10
Posit
Posit
Score 9.1 out of 10
Enterprises
Dataiku
Dataiku
Score 8.6 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
IBM SPSS ModelerMLReef
Likelihood to Recommend
10.0
(6 ratings)
9.1
(1 ratings)
Support Rating
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
IBM SPSS ModelerMLReef
Likelihood to Recommend
IBM
Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.
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MLReef
Works well if you have to involve different roles in different organizations in a project. Less suited when you have a complex system of custom developed tools
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Pros
IBM
  • Combine text and data
  • Provide facilities for all phases of the data mining process.
  • Use a node and stream paradigm to easily and quickly create models.
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MLReef
  • Helps us to take on more client projects
  • Can be used by data analysts as well as casual users
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Cons
IBM
  • Has very old style graphs, with lots of limitations.
  • Some advanced statistical functions cannot be done through the menu.
  • The data connectivity is not that extensive.
  • It's an expensive tool.
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MLReef
  • Out of the box support for major cloud vendors
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Support Rating
IBM
The online support board is helpful and the free add ons are incredibly appreciated.
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MLReef
No answers on this topic
Alternatives Considered
IBM
When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.
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MLReef
No answers on this topic
Return on Investment
IBM
  • Positive - Ease of decision making and reduction in product life cycle time.
  • Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
  • Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.
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MLReef
  • We can handle 4 to 6 times more projects at the same time with our team
  • We stay engaged with our customers well beyond the project duration
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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.

MLReef Screenshots

Screenshot of ML Pipeline creation - from fully flexible git repositories to addressable, explorable and easy accessible drag-and-drop elementsScreenshot of A knowledgebase for your organization: ML Projects and AI Modules (scripts)Screenshot of Full version control and transparent experiment trackingScreenshot of Repositories to manage your scripts (SCM) and data (pipelines)Screenshot of Manage your team, groups and projects with access rights and granular permissions