Data Fingerprinting to enable Incremental Improvement in Machine Learning Complexity

Introduction

Many startups would like to incorporate a machine learning component into their product(s). Most of these products are unique in terms of the business, the data that is required to train the machine learning models, and the data that can be collected. One of the main challenges that these startups have is the availability of data specific to their business problem. Unfortunately, the quality of the machine learning algorithms is dependent on the quality of the domain specific data that is used to train these models. Generic data sets are not useful for the unique problems that these startups are solving. As a result, they cannot rollout a feature involving machine learning until they can collect enough data. On the other hand, customers ask for the product feature before their usage can generate the required data. In such a situation, one needs to rollout a machine learning solution incrementally. For this to happen, there must be a synergy between the data and the algorithms that have the ability to process this data. To enforce this synergy, we propose a computational model that we refer to as “Data Fingerprinting”.