AI and data science can transform a business, but the reality is most projects fail before modeling even begins despite investments in teams, technology, and infrastructure.
Rasgo’s Accelerated Modeling Preparation Platform (AMPP) specializes in accelerating the preparation workflow for Data Scientists alongside the tools they already know and love.
Maximize the number of models data scientists can train, evaluate, and deploy. Complete more projects on time and on budget.
A central repository enables feature discovery, reuse and collaboration with teammates.
Simple features are automatically created & user defined functions enable rich transformations.
Data issues are automatically surfaced along with resolutions to expedite data cleansing.
Identify and create join keys between disparate data sources with no manual effort.
Explore and analyze features with auto-generated profiles and visualizations.
Access training data in your preferred data science platform and tools.
Automatically surface raw data for immediate visibility.
/ Write code to extract & load raw data into a development environment.
Explore and analyze features immediately with automatically generated profiles and visualizations.
/ Write code to profile raw data & generate visualizations.
/ Explore & analyze raw data.
Instantly surface potential data issues with recommended resolutions for the user to accept or modify.
/ Manually identify, troubleshoot & resolve data issues via custom code.
Access reusable pipelines to standardize and denormalize feature data.
/ Write code to flatten & denormalize data.
Auto-join features from multiple data sources when compatible. Approve join keys with full transparency.
/ Research & identify proper join keys.
/ Write code to create join keys from raw columns.
/ Evaluate join results & troubleshoot.
Access reusable feature transformations for your specific model and features.
/ Write code to implement necessary transformations for the current model.
Feature repository makes it fast and easy to save, search, reuse, and share features.
/Track down and review existing code from colleagues or old modeling projects (if possible) to identify any relevant feature code.
Start with features in your repository and write code to create new features as necessary. Save new features for future projects or teams.
/ Write code to create new features.
Instantly analyze relationships between your features and target variable. Evaluate correlation between your new features and the target variable.
/ Write code to perform initial correlation analysis and tests for feature significance.
Prune, modify, or add additional features to your model based on insights gleaned from correlation analysis.
/ After you train your model, you'll have to come back here to modify your features or create some more.
Instantly access training data (automatically assembled for you based on your feature definitions) directly in your notebook or model training environment.
/ Run all feature code against full dataset to generate training data.
Learn the secrets behind over 20 different models with Rasgo’s Model Accelerators.
Reduce churn by predicting which customers are likely to churn and why.
Determine which marketing campaigns drive a customer to purchase.
Predict future demand for services, such as sessions or downloads.
More accelerators designed to ramp up your ML projects.