Build and retrain hundreds of predictive models across multiple segments – quickly and easily. Then automatically pick the best model for each segment. SAS Factory Miner enables modelers and statisticians to work more efficiently, which gives them more time to unearth valuable insights buried in granular segments. Such insights can reveal new opportunities, expose hidden risks and fuel smarter, well-timed decisions.
Boost model building productivity.
Reap huge productivity gains by automating time-consuming model development processes – including data prep, variable transformation, predictor variable, algorithm selection, etc. SAS Factory Miner has an easy-to-use, web-based interface that lets you build multiple models for each segment, and automatically identify the most accurate one.
Automate model development.
Choose the best segmentation strategy to solve your business problems. And jump-start your predictive modeling with prebuilt model building templates that you can customize to fit your needs. Automated reporting and documentation make it easy to share best practices on model design and results across your organization.
Explore new ideas faster.
Apply machine learning and predictive analytics techniques to large, complex data sets, and get the results fast. If a model fails, you can try again quickly using different inputs or ideas. As variables change or new variables are found, you can test them without having to rebuild the entire data mining flow or challenge an existing set of algorithms.
Put models into operation quickly.
Deploy champion models in different production environments with just the click of a button. SAS Factory Miner automatically generates complete scoring code – including all necessary data prep and transformation steps. And retraining models is easy because all assets related to model development and deployment are centrally managed and accessible via REST endpoints.
- Data preparation. Includes analytical data preparation capabilities.
- Customizable model templates. Provides out-of-the-box model building templates that can be customized and shared across projects and users.
- Self-service machine learning techniques. Includes:
- Linear regression.
- Logistic regression.
- Decision trees.
- Random forests.
- Generalized linear models.
- Gradient boosting.
- Neural networks.
- Bayesian networks.
- Support vector machines.
- Champion model identification. Uses a variety of interactive assessment techniques.
- Model exception identification. Enables you to fine-tune models.
- Model retraining. Lets you retrain models over time using new data and variables, including REST endpoints.
- Scalable processing. Runs analytical procedures on a single machine, via grid computing or in-memory processing.
- Flexible model deployment. Lets you deploy models in database or in Hadoop to score new data using SAS Scoring Accelerator.
- Add-on to SAS® Enterprise Miner™. SAS Factory Miner runs as an add-on to SAS Enterprise Miner.
For more information, read the SAS Factory Miner fact sheet.
Learn why Gartner names SAS a Leader in the the Magic Quadrant for Data Science Platforms.
Got questions about data scientists? Get answers in our special Data Scientist Series.