Get hands-on with Gooder AI. This webpage has everything you need to get started with Gooder AI and realize the business potential of your ML models. Begin with these introductory videos:

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Why must predictive AI be valuated? Also view this short video series.

Use Gooder AI: easy, fast, free

First, if you're not familiar with Gooder AI, ML valuation, and the advantage of planning machine learning deployment by viewing its potential business value, begin with the introductory videos above. Then read the Gooder white paper.

Second, try Gooder AI hands-on with a single click – choose from several publicly-available models.

Third, get started using Gooder AI on your own model by watching the series of short hands-on, how-to videos below.

About

Videos

Watch the first hands-on, how-to video:

Watch the full playlist of how-to videos, which includes:

Supercharging your predictive AI projects
How to valuate XGBoost and scikit-learn models
Customizing #MLvaluation for each predictive AI project
A breakthrough for predictive AI: driving decisions with "expected value"
A crystal-clear business view into predictive AI projects
Optimizing the decision threshold for predictive AI deployment

Or start with this short, 5-minute overview of using Gooder hands-on.

Supporting links and files

Open the Gooder AI product: app.gooder.ai

Download the standard starter configuration file: docs.gooder.ai

Documentation: Overview of standard earnings curves/metrics – this PDF summarizes the earnings metrics available within the starter config

Documentation: Gooder AI in Python for XGBoost, scikit-learn, PyTorch, and CatBoost models – this includes a sample notebook for use in Jupyter and a version for use in Databricks (info about the Databricks / Gooder AI partnership)

Datasets and associated config files used in the how-to videos:

Support: Contact Gooder AI