why data science projects fail

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Feature engineering without frustration.

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Data Exploration
Select and integrate data from one or more sources and determine the data’s applicability to addressing the business use case.
Prepare data for feature engineering and model training, including data cleansing, joining disparate data sources, and transforming data.
Data Preparation
Write python or SQL code to translate the data into machine learning features that capture and distill signal from the raw values.
Feature Engineering
Model Training
Implement different algorithms to train various ML models.
Model Evaluation
Evaluate models. Determine model quality metrics.
Deploy model for scoring via batch or API inputs.
Model Deployment
The problem

AI and data science can transform a business, but the reality is most projects fail before modeling even begins because Data Scientist's get bogged down wrangling and hen preparing data for feature engineering.

Rasgo acceleration

Rasgo accelerates data science and improves model accuracy by integrating feature engineering, data exploration, and data preparation workflows in one powerful, Data Scientist-friendly platform.

Model training begins

Improve model accuracy by maximizing the number of models data scientists can train, evaluate, and deploy. Get more accurate models deployed to production on time and on budget.

Meet your feature's new pre-training regimen.

Feature repository

A central repository enables feature discovery, reuse and collaboration with teammates.

Rasgo app feature repository page
Feature transformations

Simple features are automatically created & user defined functions enable rich transformations.

Cleaner data

Data issues are automatically surfaced along with resolutions to expedite data cleansing.

Automatic joins

Identify and create join keys between disparate data sources with no manual effort.

Feature profiles

Explore and analyze features with auto-generated profiles and visualizations.

Ragso app feature profile page
Train in your tools

Access training data in your preferred data science platform and tools.

0 hrs 0 mins
Typical workflow
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01 / Extract & Explore

20 mins

Automatically surface raw data for immediate visibility.

2 hrs

/ Write code to extract & load raw data into a development environment.

3 hrs

Explore and analyze features immediately with automatically generated profiles and visualizations.

Feature profiles
Rasgo app feature repository page

9 hrs

/ Write code to profile raw data & generate visualizations.

/ Explore & analyze raw data.

02 / Data Preparation

1 hr

Instantly surface potential data issues with recommended resolutions for the user to accept or modify.

Data transformations

5 hrs

/ Manually identify, troubleshoot & resolve data issues via custom code.

5 mins

Access reusable pipelines to standardize and denormalize feature data.

Cleaner data

2 hrs

/ Write code to flatten & denormalize data.

5 mins

Auto-join features from multiple data sources when compatible. Approve join keys with full transparency.

Automatic joins

4.5 hrs

/ Research & identify proper join keys.

/ Write code to create join keys from raw columns.

/ Evaluate join results & troubleshoot.

10 mins

Access reusable feature transformations for your specific model and features.

2 hrs

/ Write code to implement necessary transformations for the current model.



Feature repository makes it fast and easy to save, search, reuse, and share features.

Feature repository
Rasgo app feature repository page

8 hrs

/Track down and review existing code from colleagues or old modeling projects (if possible) to identify any relevant feature code.

3 hrs

Start with features in your repository and write code to create new features as necessary. Save new features for future projects or teams.

4 hrs

/ Write code to create new features.

10 mins

Instantly analyze relationships between your features and target variable. Evaluate correlation between your new features and the target variable.

Feature profiles
Rasgo app feature repository page

1 hr

/ Write code to perform initial correlation analysis and tests for feature significance.

2.5 hrs

Prune, modify, or add additional features to your model based on insights gleaned from correlation analysis.

12 hrs

/ After you train your model, you'll have to come back here to modify your features or create some more.

5 mins

Instantly access training data (automatically assembled for you based on your feature definitions) directly in your notebook or model training environment.

3 hrs

/ Run all feature code against full dataset to generate training data.

Get your features going.

Learn the secrets behind over 20 different models with Rasgo’s Model Accelerators.

Customer churn analysis

Reduce churn by predicting which customers are likely to churn and why.

Marketing attribution

Determine which marketing campaigns drive a customer to purchase.

Digital demand forecasting

Predict future demand for services, such as sessions or downloads.



More accelerators designed to ramp up your ML projects.

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Accelerate feature engineering with Rasgo.

Feature Highlight
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Rasgo app feature repository page