SAS Visual Data Mining and Machine Learning Reviews

It's great to be able to build machine learning models in the same environment where dashboards are created.

Models created in this environment cannot have the same complexity as models created in Model Studio.

It allows users to interactively explore data, uncover insights, and build models through pipelines or programming.

Visual modeling without code: The user interface allows creating machine learning models via drag and drop. This is particularly useful for analysts who are not necessarily programmers but still want to create complex models.

AutoML functionality: SAS VML can automatically create multiple models, compare them, and suggest the best model - including transparency in evaluation and selection.

Since VML is designed for no-code usage, the integration of user code, especially in languages foreign to the platform, is not particularly well supported (e.g., no syntax highlighting in the code editor).

This URL-based application is beneficial for creating a dashboard as an output of the ML models produced by business users. Basic and some of the complex preprocessing of the data, like cleaning, manipulation, and validation, can also be done on the same interface page itself. Along with Models, Regular data visualization of the data can also be performed. And can be shared via URL for another business user to view the dashboard without any pre-installation or any sort

This application is primarily ideal only for the structured data source. Only a limited range of model creations, like linear and logistic regression, NN, and Forest, is available. Even though this application combines ETL, data analytics and visualization, extensive data cleaning is not possible in the same interface, as it is suitable only for structured data.

Best for the combination of live data from IoT sensors and prediction of fault or for forecasting.

For generating reports and sharing widely within the team via

SAS Visual Data Science Decisioning is one of the best integrated Environment for data preparation, exploration, modeling, and deployment. Its inbuilt machine learning features helped us in implementing data science project very faster.

It has helped us in making machine learning or predictive modelling for predicting the response rate for our marketing analytics project by analysing the bureau data. It is easy to use as well once one gets usual with the environment.

There is nothing dislike about SAS Visual Data Science Decisioning but SAS can make the licence of this tool at lower cost.

SAS Visual Data Science Decisioning has helped us in implementing predictive modelling for predicting the response rate for our marketing analytics project by analysing bureau data.

It is one of the best cloud-based and in-memory analytics software in the market. It is very easy for use who has already worked on SAS Studio or SAS enterprise Guide. One can easily connect it with Teradata or Hadoop for large data processing . It has inbuilt predictive analytics methodology like logistic regression or decision Tree and one can easily implement this without writing codes. In addition to this, it has an inbuilt feature for Data visualization as well .

Sometimes, it get stuck or get hanged while multiple users are working on this in the login page of SAS Via otherwise its very easy to use and work for statistical analyst.

There are multiple problems which SAS Via Platfrom has helped me like predicting the loan defaulter in credit card analytics and helps busniess in minimising the Net credit loss rate.

In adition to this, It has also helped in creating customer segmentation through its decision Tree feature in creating high vs low risky customers by analysing customer bureau data.

In SAS Visual data mining and machine learning tool, we can easy to prepare different models like predictive models, time series models, etc. Using SAS VDMML we can create open source models also.

It's the best analytic tool for modeling, but the proper study materials are unavailable to all the new developers or students that are interested to learn SAS VDMML.

SAS Visual Data Mining and Machine Learning are not available for new persons, but it's the best tool for analytics. The results are also very accurate with the proper visualization.

SAS provides the complete inbuilt algorithm for data curation, macro programming, advanced analytics, AI and machine learning models to through SAS Viya and Visual Data Mining and Machine Learning with just few lines of code.

Need very thorough understanding of the ML & AI model before implementing and interpretng the outcome.

Easy to preprocess data and perform feature engineering, required very less coding to create supervised machine learning models and assess model performance. Able to use pipelines to generate forecast and select champion pipelines.

I am using SAS Viya from last year and it can handle big data very well. I did different reporting and dashboards, super easy to use and quick. I have also used ML functionality of platform and its very powerful as well.

I will say hosting cost, but as it is powerful tool it require more resources.

We work for clients problem on SAS Viya and it helps to resolve it very well.

The platform's commitment to data governance, security, and compliance is commendable. Its user-friendly interface, which simplifies complex analytics tasks, making them accessible to users of varying skill levels

Minor bug fixes, customization options. Cost

Derive customer insights and analyzing day today problems

Intuitive and engaging UI makes the non technical users life much easier. The data analytics background within SAS adds much needed value addition to this suite which makes ML modeling tasks more resourceful and wholesome.

The repository of various ML techniques in the library needs an overhaul. Maybe that might be up in the cards sooner than before. The availability of more generic tasks like anomaly detection, outlier detection, time series Forecasting would definitely make the competition turn around.

Predictive analytics is probably the industry standard issue which has come to the forefront with the advent of industry 4.0. Predictive maintenance is a byproduct which definitely packs a punch in the arsenal of benefits.