IBM Machine Learning for z/OS® brings AI to transactional applications on IBM zSystems. It can embed machine learning and deep learning models to deliver real-time insight, or inference every transaction with minimal impact to operational SLAs.
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SAS Enterprise Miner
Score9 out of 10
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SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
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Pricing
IBM Machine Learning for z/OS
SAS Enterprise Miner
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
IBM Machine Learning for z/OS
SAS Enterprise Miner
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Features
IBM Machine Learning for z/OS
SAS Enterprise Miner
Platform Connectivity
Comparison of Platform Connectivity features of IBM Machine Learning for z/OS and SAS Enterprise Miner
Feature
IBM Machine Learning for z/OS
-
Ratings
SAS Enterprise Miner
8.8
4 Ratings
5% above category average
Connect to Multiple Data Sources
00 Ratings
8.14 Ratings
Extend Existing Data Sources
00 Ratings
9.04 Ratings
Automatic Data Format Detection
00 Ratings
9.34 Ratings
MDM Integration
00 Ratings
9.02 Ratings
Data Exploration
Comparison of Data Exploration features of IBM Machine Learning for z/OS and SAS Enterprise Miner
Feature
IBM Machine Learning for z/OS
-
Ratings
SAS Enterprise Miner
8.1
4 Ratings
4% below category average
Visualization
00 Ratings
7.14 Ratings
Interactive Data Analysis
00 Ratings
9.14 Ratings
Data Preparation
Comparison of Data Preparation features of IBM Machine Learning for z/OS and SAS Enterprise Miner
Feature
IBM Machine Learning for z/OS
-
Ratings
SAS Enterprise Miner
8.0
4 Ratings
2% below category average
Interactive Data Cleaning and Enrichment
00 Ratings
7.84 Ratings
Data Transformations
00 Ratings
8.24 Ratings
Data Encryption
00 Ratings
8.12 Ratings
Built-in Processors
00 Ratings
8.12 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of IBM Machine Learning for z/OS and SAS Enterprise Miner
Feature
IBM Machine Learning for z/OS
-
Ratings
SAS Enterprise Miner
8.8
4 Ratings
4% above category average
Multiple Model Development Languages and Tools
00 Ratings
7.54 Ratings
Automated Machine Learning
00 Ratings
9.82 Ratings
Single platform for multiple model development
00 Ratings
8.54 Ratings
Self-Service Model Delivery
00 Ratings
9.23 Ratings
Model Deployment
Comparison of Model Deployment features of IBM Machine Learning for z/OS and SAS Enterprise Miner
IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
SAS Enterprise Miner is world-class software for individuals interested in developing reproducible models in a reasonable amount of time. Perhaps the most useful part of SAS Enterprise Miner is the ability to compare models with other models without writing code. The ensemble modeling capabilities is the easiest way to do ensemble modeling I have come across. SAS Enterprise Miner is well-suited for beginning to advanced analysts who know something about advanced analytics. The software is not well-suited for analysts or companies that have little interest in advanced modeling.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
SAS' customer support used to be non-existent many years ago. Today, contacting SAS customer support is great. They are responsible, knowledgable, and seem to have an interest in getting the results right the first time. With that said, Enterprise Miner's online support is weak, probably because the user base is much smaller than other tools.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
In our organization, users were using SAS already so the learning curve was really low. Within a few weeks after the implementation, the users were already delivering models developed with SAS Enterprise Miner. It is difficult to talk about ROI as models were already being developed before. It was mostly a change of technology and it was a smooth transition.
Going with Enterprise Miner came with migration from desktop use of SAS to a server use of SAS. This created a new role of SAS administrator. This was obviously a cost but as the use of SAS increased greatly, it was expected.
From a methodology standpoint, Enterprise Miner helped greatly in the documentation of the model development which was a requirement in a few groups such as the risk groups. Having a visual "GUI-like" approach to development, the flowchart or diagram of the project in Miner was able to give users a good understanding of the approach the analyst took to develop the model.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info