SAS Visual Machine Learning

Automatically generates insights, including summary reports about a project and champion and challenger models. Simple language from embedded natural language generation facilitates report interpretation and reduces the learning curve for business analysts.

You can embed open source code within an analysis, call open source algorithms within a pipeline, and access those models from a common repository – seamlessly within Model Studio. This facilitates collaboration across your organization, because users can do all of this in their language of choice.

Superior performance from massive parallel processing and the feature-rich building blocks for machine learning pipelines let you explore and compare multiple approaches rapidly. Quickly and easily find the optimal parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option you want.

Data scientists, business analysts and other analytics professionals get highly accurate results from a single, collaborative environment that supports the entire machine learning pipeline. A variety of users can access and prepare data. Perform exploratory analysis. Build and compare machine learning models. Create score code for implementing predictive models. Execute one-click model deployment. And do it all faster than ever before with our automated modeling API.

To enhance collaborative understanding, the solution provides all users with business-friendly annotations within each node describing what methods are being run, as well as information about the methods, results and interpretation.

Standard interpretability reports are available in all modeling nodes, including LIME, ICE, Kernel SHAP, PD heatmaps, etc., with explanations in simple language from embedded natural language generation. Export modeling insights as a PDF report that can be shared outside the data science team.

Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.