A guide to minimizing risks when deploying machine learning systems within a corporate environment.
The Statistics of Failure:
More than 80% of ML projects never reach the stage of industrial operation. The primary reason is the gap between business expectations and the quality of available data. Companies often attempt to build a “rocket” without having high-quality “fuel.”
The RnD-42 Approach: The Research Pilot
We have formalized the project entry stage into a 60-day cycle:
- Data Audit: Assessing data entropy, identifying gaps, and detecting anomalies.
- Hypothesis Testing: Verifying the fundamental possibility of predicting target indicators based on historical data.
- Proof of Concept (PoC): Developing a Minimum Viable Product (MVP) that demonstrates clear business value.
Executive Summary:
This methodology allows the client to invest in large-scale development only after the system’s Return on Investment (ROI) has been mathematically proven. It transforms AI implementation from a “lottery” into a predictable process of IT landscape evolution.