SAS® Visual Data Mining and Machine Learning
SAS® Visual Data Mining and Machine Learning
Everything you need to solve the most complex analytical problems – in a single, integrated, collaborative solution.
SAS Visual Data Mining and Machine Learning supports the end-to-end data mining and machine-learning process with a comprehensive, visual (and programming) interface that handles all tasks in the analytical life cycle. It suits a variety of users and there is no application switching. From data management to model development and deployment, everyone works in the same, integrated environment.
Solve complex analytical problems faster.
This solution runs on SAS® Viya®, the latest addition to the SAS Platform, delivering predictive modeling and machine-learning capabilities at breakthrough speeds. 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.
Empower users with language options.
Don't know SAS code? No problem. Python, R, Java and Lua programmers can experience the power of this solution without having to learn how to program in SAS – with access to trusted and tested SAS machine learning algorithms you can use from other languages. You can also quickly deploy your predictive models with automatically generated SAS score code. Shorten the time to value even more with easy-to-implement score code that is automatically generated in multiple programming languages for all your machine-learning models.
Explore multiple approaches quickly to find the optimal solution.
Superior performance from distributed processing and the feature-rich building blocks for machine-learning pipelines let you quickly explore and compare multiple approaches. Automated model tuning lets you explore and compare multiple scenarios quickly and easily in an integrated environment to find the best-performing model. You can combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types. And reproduceability in every stage of the analytical life cycle delivers answers and insights you can trust.
Boost the productivity of your analytical teams.
Data scientists and other analytical professionals can get highly accurate results from a single, collaborative environment that supports the entire machine-learning pipeline. The solution enables a variety of users to 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.
Reduce latency between data and decisions.
Interactive visual and programming interfaces dramatically shorten the time it takes to prepare data and build sophisticated machine-learning models. Users can share data sources and code snippets for improved collaboration. You can code projects using SAS or other languages, such as Python, R, Java or Lua. The SAS code is generated behind the scenes for later batch runs, editing and automation. You can also create advanced machine learning algorithms using a visual drag-and-drop interface without ever having to code.
- A flexible, approachable visual environment for analytics. Multiple users can currently analyze any amount of structured and unstructured data with the easy-to-use visual interface.
- Highly scalable, in-memory analytical processing. A secure, multiuser environment enables concurrent access to data in memory. Data and analytical workload operations are distributed across nodes, in parallel, and are multithreaded on each node for very fast speed.
- Innovative statistical, data mining and machine-learning techniques. An incredibly broad set of modern statistical, machine learning, deep learning and text analytics algorithms are all accessible within a single environment. Analytical capabilities include clustering, different flavors of regression, random forests, gradient boosting models, support vector machines, natural language processing, topic detection and more.
- Integrated data preparation, exploration and feature engineering. A drag-and-drop interface enables data engineers to quickly build and run transformations, augment data and join data within the integrated visual pipeline of activities. All actions are performed in memory to maintain a consistent data structure.
- Integrated text analytics. Because the solution is designed with big data in mind, you can examine extremely large collections of text documents. Explore all of your textual data, not just a subset, to gain new insights about unknown themes and connections.
- 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. Then operationalize analytics in distributed and traditional environments with automatically generated SAS score code.
- Accessible and cloud-ready. Whether it’s Python, R, Java or Lua, modelers and data scientists can access SAS capabilities from their preferred coding environment, whether it's Python, R, Java or Lua. And with SAS Viya REST APIs, you can add the power of SAS to other applications.
Read the SAS Visual Data Mining and Machine Learning fact sheet for more details.
SAS is a Leader in The Forrester Wave™: Predictive Analytics and Machine Learning Solutions, Q1 2017.
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