Data comes in all shapes and sizes, yet unlocking actionable insight efficiently requires deep knowledge of data science techniques. The Analytics and Machine Learning Collection provides a comprehensive set of machine learning and data modeling capabilities to streamline your data science initiatives.
Analyze data, train and retrain models, and deploy your automated solution to useful enterprise applications.
Developing machine learning solutions often requires complex software architectures and deep statistical knowledge. With the Analytics and Machine Learning Collection, developers and end users alike can incorporate the latest machine learning techniques to their workflows with just a few clicks. No coding required.
The Machine Learning Collection
Leverage a range of machine learning methods
XGBoosting, Genetic Function Approximation (GFA), Random Forests, convolutional neural networks and more
Rapidly apply statistical analyses
Explore data with regression, partial least squares, Bayesian statistics to conduct statistical hypothesis testing
Support for 3rd party statistic platforms and tools such as Jupyter Notebook, R, JMP and SAS
Read in discipline-specific data
Purpose-built to support various numerical, chemical, biological, textual, and image data types
Curate model performance
Deploy model applicability domain (MAD) methods and cross-validation
Train multiple trial models in parallel to identify top performers or combine multiple models into a single ensemble model
Simplify multi-objective optimization
Employ methods such as Pareto sorting with a few clicks
Visualize results in workflow
Generate interactive reports with ROC plots, enrichment plots and other visualization techniques