SAS Visual Data Mining and Machine Learning

What differentiates SAS Visual Data Mining and Machine Learning from other similar products?

It allows an end to end work flow from data acess, preparation and modelling, building, and validation. Ease of integration within the SAS ecosystem

What is your favorite aspect of this product?

The drag and drop interface for modelling. I also like the R and python integration

What do you dislike most about this product?

The community support is not very vast compared to other product

What recommendations would you give to someone considering this product?

Invest in early tarining

What differentiates SAS Visual Data Mining and Machine Learning from other similar products?

End-to-End Platform: SAS VDMML provides a fully integrated environment for the entire machine learning lifecycle—from data preparation to model deployment—without switching between multiple tools. AutoML and Model Interpretability: VDMML includes robust AutoML capabilities that automate the model-building process, while also offering interpretability tools like partial dependency plots and SHAP (SHapley Additive exPlanations) values to understand model behavior.

What is your favorite aspect of this product?

If I were to highlight the most impactful aspect of SAS Visual Data Mining and Machine Learning (VDMML), it would be its seamless integration of advanced analytics and interpretability within an enterprise-ready environment.

What do you dislike most about this product?

The main drawback of SAS Visual Data Mining and Machine Learning (VDMML) is its steep learning curve and interface complexity, especially for users who are not already familiar with SAS's ecosystem. Unlike more modern, streamlined platforms like DataRobot or Azure ML, VDMML's interface can feel cluttered and overwhelming, requiring substantial training to navigate effectively.

What recommendations would you give to someone considering this product?

Understand Your Use Case: SAS VDMML excels in enterprise-scale analytics, regulated industries (like finance and healthcare), and scenarios needing strong data governance. If your projects require interpretability, large-scale processing, or robust security, it's a solid choice. Evaluate Cost vs. Value: SAS is premium-priced. Ensure the value it brings—like advanced analytics, governance, and scalability—justifies the cost compared to alternatives like Azure ML, DataRobot, or AWS SageMaker.

What differentiates SAS Visual Data Mining and Machine Learning from other similar products?

SAS Visual Data Mining and Machine Learning stands out due to its strong focus on both automation and user experience. It offers an intuitive visual interface that allows users to build, validate, and deploy models without extensive coding knowledge, making it accessible to both data scientists and business analysts. Additionally, SAS's robust data management capabilities seamlessly integrate with the analytics process, ensuring high data quality. The platform also includes advanced algorithms and machine learning techniques, supported by SAS's long-standing expertise in analytics.

What is your favorite aspect of this product?

One of my favourite features of SAS Visual Data Mining and Machine Learning is its user-friendly UI. It simplifies difficult analytics, allowing both experienced data professionals and amateurs to visualise data and construct models without becoming bogged down in coding. I really like how it blends strong algorithms with built-in automation to accelerate the modelling process while maintaining accuracy. It truly allows users to examine their data creatively and make informed conclusions swiftly.

What do you dislike most about this product?

One aspect of SAS Visual Data Mining and Machine Learning that I find frustrating is that the licensing and pricing can be expensive, particularly for smaller organisations or individual users. This may make it less accessible than some other tools on the market. Additionally, while the interface is generally user-friendly, there can still be a learning curve, especially for those who are completely new to data science. It would be great to see more resources or tutorials to help ease that transition.

What recommendations would you give to someone considering this product?

If you're thinking about using SAS Visual Data Mining and Machine Learning, my first suggestion is to try the trial version, if it's available. It's an excellent method to test the features and determine whether they match your specific requirements before making a complete commitment. Invest some time in SAS's training resources and documentation. They provide a multitude of lessons and support to help you flatten the learning curve and get the most out of the product.