Most Helpful Review
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.
It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component.
When you import the dataset you can see the data distribution easily with graphics and statistical measures.
Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most.
MLS allows me to set up data experiments by running through various regression and other machine learning algorithms, with different data cleaning and treatment tools. All of this can be achieved via drag and drop, and a few clicks of the mouse.
Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently.
Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing.
I personally would prefer if data could be tunneled to my model through a SAP ERP system, and have features of Excel, such as Pivot Tables, integrated.
It could use to add some more features in data transformation, time series and the text analytics section.
Pricing and Cost Advice
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.
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Also Known As
|Also Known As||SPSS Statistics||Azure Machine Learning|
|Overview||Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.|
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
It has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.
Learn more about IBM SPSS Statistics
Learn more about Microsoft Azure Machine Learning Studio
|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|
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