Machine learning 2.0 : Engineering Data Driven AI Products

TL;DR

This paper introduces ML 2.0, a rapid 8-week development paradigm enabling non-experts to create and deploy data-driven AI products efficiently, shifting focus from discovery to impact.

Contribution

It proposes a new streamlined process and reusable APIs for quick development and deployment of minimum viable data-driven models, reducing time and expertise barriers.

Findings

Achieves rapid model development within 8 weeks

Enables non-ML experts to deploy AI solutions

Supports refinement and adaptation of models

Abstract

ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deployment - in favor of a rapid, 8-week process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs. This accomplishes what we call a "minimum viable data-driven model," delivering a ready-to-use machine learning model for problems that haven't been solved before using machine learning. We provide provisions for the refinement and adaptation of the "model," with strict enforcement and adherence to both the scaffolding/abstractions and the process. We imagine that this will bring forth the second phase in machine learning, in which discovery is subsumed by more targeted goals of delivery and impact.