SAS^® VISUAL DATA MINING AND MACHINE LEARNING

Data access, preparation & quality

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

Custom chatbot creation

Create and deploy custom, natural language chatbots via an intuitive, low-code visual interface for chatbot-enabled insights and conversational user experiences​.

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 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.

Cutting-edge machine learning

Take advantage of reinforcement learning, either through Fitted Q-Networks or Deep Q-Networks, 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 your 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.

Easy-to-use analytics

Best practices 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.

Powered by SAS^® Viya^®

SAS Viya has a completely redesigned architecture that is compact, cloud native and fast. Whether you prefer to use the SAS Cloud or a public or private cloud provider, you'll be able to make the most of your cloud investment.