SAS for Machine Learning and Deep Learning
Enable everyone to work in the same integrated environment – from data management to model development and deployment.
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
Recommended resources for SAS for Machine Learning and Deep Learning
To browse resources by type, select an option below.
-
- Select resource type
- E-BOOK
- R E S E A R C H
- 개요 브로셔
- 경영진 요약정보
- 고객 사례
- 기사
- 동영상
- 백서
- 백서
- 분석 보고서
- 블로그 게시물
- 사례 연구
- 산업 개요
- 솔루션 요약
- 시리즈
- 웨비나
- 인포그래픽
- 제품 요약
- 책 발췌
- 팩트 시트
- 회견
- 고객 사례 Finland’s top retail bank applies AI to improve customer service and credit scoring
- 고객 사례 Transforming the consumer banking experience through advanced analytics
- 고객 사례 Building reliability in risk
- 고객 사례 It’s all in the research: Using AI to solve issues in health care
- 고객 사례 Jakarta Smart City uses IoT analytics to better serve residents
- 고객 사례 Improving data collection and modeling to accelerate predictive medicine efforts
- 고객 사례 Revolutionizing marketing campaigns with AI
- 블로그 게시물 Building and comparing open source models in SAS Model Studio
- 블로그 게시물 The analytical platform modernization
- 백서 Statistics and Machine Learning at Scale
- 백서 The Machine Learning Landscape
- 백서 How to Do Deep Learning With SAS®
- 분석 보고서 SAS, 가트너 MQ ‘데이터 사이언스·머신러닝 플랫폼’ 부문 7년 연속 리더
분석 분야의 리더인 SAS 솔루션으로 시작하십시오.