SAS® Visual Data Mining and Machine Learning
SAS® Visual Data Mining and Machine Learning
Huge performance gains. Innovative algorithms.
One in-memory environment.
An intuitive programming environment. Innovative algorithms. Fast, in-memory processing. SAS Visual Data Mining and Machine Learning shatters barriers related to data volume and variety, limited analytical depth and computational bottlenecks. That means greater productivity – and faster, deeper insight.
Solve complex analytical problems faster.
Get breakthrough performance from predictive modeling and machine-learning capabilities running on SAS® Viya™, our new in-memory platform. In-memory data persistence eliminates the need to load data multiple times during iterative analysis. Multiple users can collaborate easily, exploring the same raw data and building models simultaneously. You'll measure processing time for analytical modeling in seconds or minutes – not hours – and find solutions to challenging problems faster than ever.
Access powerful SAS® Analytics from an open platform.
Don't know SAS code? No problem. Your open source programming skills are all you need to experience the power of SAS advanced analytics. Whether you’re using a Python notebook, Java client or Lua scripting interface, you can call SAS Analytics using your preferred language in our world-class, governed environment. And using REST APIs, you can add the power of SAS Analytics to your custom applications.
Explore multiple approaches quickly to find the optimal solution.
The superior performance of the distributed analytical engine and feature-rich building blocks for your machine-learning pipeline enable you to explore and compare multiple scenarios quickly and easily. Automated tuning lets you test different scenarios in an integrated environment to identify the best-performing model and find accurate answers.
Boost data scientist productivity.
Data scientists and other analytical professionals will reap huge productivity gains from the solution's single, integrated in-memory environment. They can access and prepare data. Perform exploratory analysis. Build and compare machine-learning models. Create score code for implementing predictive models. And they can do all this faster than ever before.
Use a graphical interface for common machine-learning tasks.
A web-based programming environment provides intuitive graphical interfaces, enabling easy configuration of common machine-learning tasks. The associated SAS code is generated automatically for later batch runs and automation. Users can share data sources and code snippets in this environment, which fosters greater collaboration.
- A flexible, web-based programming environment. An interactive, web-based interface lets you perform the most common machine-learning steps – from data prep to model building, assessment and scoring.
- Highly scalable, in-memory analytical processing. Optimized for multipass analytical computations, the analytics processing engine provides a secure, multiuser environment for concurrent access to data in memory.
- Powerful data manipulation and management. The same distributed, in-memory environment is used for preparing data for analytics – accessing data, joining tables, subsetting and filtering data, and creating the final table for machine-learning projects.
- Data exploration, feature engineering and dimension reduction. Descriptive statistics and powerful graphical programming let you find and fix data issues, quickly identify potential predictors, reduce large data set dimensions and easily create new features from original data.
- Open platform. Open APIs let you call SAS Analytics using a variety of programming languages – without having to code in SAS. Or add SAS Analytics to your custom applications using SAS Viya REST APIs.
- Modern statistical, data mining and machine-learning techniques. The solution includes powerful unsupervised- and supervised-learning algorithms, such as clustering, principal component analysis, linear and nonlinear regression, logistic regression, decision trees, random forests, gradient boosting, neural networks, factorization machines and support vector machines.
- Automatic tuning. Powerful optimization engines enable you to tune hundreds of model parameters for the most accurate results.
- Integrated text analytics. Integrated text analytics capabilities include powerful text preprocessing, natural language processing, topic detection and more.
- Model assessment and scoring. Test different modeling approaches in a single run and compare results of multiple supervised-learning algorithms with standardized tests to quickly identify champion models.