Solve the most complex analytical problems using machine learning and deep learning via a single, integrated, collaborative solution with its own automated modeling API

Screenshot of SAS VDML Pipeline with highlight

SAS for Machine Learning and Deep Learning

Enable everyone to work in the same integrated environment – from data management to model development and deployment.


Customers working smarter with SAS


Key features

A comprehensive visual – and programming – interface supports the end-to-end data mining and machine learning process. Analytics team members of all skill levels are empowered to handle all analytics life cycle tasks in a simple, powerful and automated way.

Data access, preparation & quality

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

Data visualization

Visually explore data, and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data​.

Synthetic data generation

Take advantage of generative adversarial networks (GANs) to generate synthetic data, both image and tabular, for your deep learning models.

Automated insights & interpretability

Automatically generate 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. Share modeling insights via a PDF report.

Bias detection

Assess models for both performance and results bias relative to specified groups.

Cutting-edge machine learning

Take advantage of reinforcement learning – through Fitted Q-Networks, Deep Q-Networks or Actor-Critic – to solve sequential decision-making problems, with support for custom environments.

Decision trees under your control

Interactively adjust the splitting and pruning of decision tree nodes to reflect your business knowledge and enforce regulatory constraints.

Automated feature engineering & modeling

Save time and improve productivity. Automated feature engineering selects the best set of features for modeling by ranking them to indicate their importance in transforming data. Visual pipelines are dynamically generated from your data, yet editable to remain a white box model.

Public API for automated modeling

Take advantage of the public API for automated modeling for end-to-end model development and deployment simply by choosing the automation option. Or use this API to build and deploy your own custom predictive modeling applications. See examples on developer.sas.com.

Deep learning with Python & ONNX support

Python users can access high-level APIs for deep learning functionalities within Jupyter notebooks via the SAS Deep Learning with Python (DLPy) open source package on GitHub. DLPy supports the Open Neural Network Exchange (ONNX) for easily moving models between frameworks. Score new data sets using ONNX models in a variety of environments by taking advantage of Analytic Store (ASTORE).

Easy-to-use analytics

Best practice templates enable a quick, consistent start to building models, ensuring consistency among the analytics team. Analytical capabilities include clustering, different types of regression, random forest, gradient boosting models, support vector machines, natural language processing, topic detection, etc.

Network analytics

Augment data mining and machine learning approaches using a versatile set of network algorithms to explore the structure of networks – social, financial, telco and others – that are explicitly or implicitly part of business data.

Highly scalable in-memory analytical processing

Get concurrent access to data in memory in a secure, multiuser environment. Distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.

Computer vision & biomedical imaging

Acquire and analyze images with model deployment on server, edge or mobile. Supports the end-to-end flow for analyzing biomedical images, including annotating images.

Code in your language of choice

Modelers and data scientists can access SAS capabilities from their preferred coding environment – Python, R, Java or Lua – and add the power of SAS to other applications with SAS Viya REST APIs.

Cloud native

SAS Viya's architecture is compact, cloud native and fast, enabling you to make the most of your cloud investment regardless of your cloud provider.

See SAS for Machine Learning and Deep Learning in action

SAS Viya is cloud-native and cloud-agnostic

Consume SAS how you want – SAS managed or self-managed. And where you want.

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