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Data Iceberg Model for Machine Learning

One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset. The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.

One of the pitfalls to developing production-ready machine learning solutions is the failure to identify the appropriate data assets. In evaluating the data assets to be used for your project, use the Data Iceberg Model approach to determine the underlying (i.e. not visible) structures that triggered the creation of the dataset.

The Iceberg Model is a good tool for discovering the underlying patterns, structures, and behaviors that cause an observable event. We know that approximately 90% of an iceberg is underwater. The 90% of the iceberg that exists below the surface is what creates the “event” seen by the 10% that exists above the surface.

The following Data Iceberg Model can be used to evaluate the quality & limitations of the data used to train your machine learning models.

bigThinking Data Iceberg Model (bT Data Iceberg Model PDF Version)

Kishau Rogers

Kishau Rogers is the editor and founder of the bigThinking project. bigThinking is a resource and collaborative innovation center which promotes the principles of systems thinking. Our mission is to empower the next generation of innovators to think bigger, to think better, and to create solutions that make a significant impact in the areas that matter. Kishau Rogers is an award-winning entrepreneur with a deep background in Computer Science, over twenty-five years of experience in the technology industry, and more than 15 years of entrepreneurial leadership. She currently serves as the Founder & CEO of Time Study, Inc., a high-growth startup offering solutions for using machine learning, advanced natural language processing, and data science to automatically tell a story of how enterprise employees spend their time.

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