Insights By Scott Ernst

Capstone Project 1

TRUST

I’ll never ask anyone to trust me. I’ll only ask a person for the opportunity to EARN their trust. Trust is based on integrity and competency.

Project Context & Purpose

The primary purpose of Capstone Project 1 is to demonstrate professional competency in the end-to-end Machine Learning Operations (MLOps) lifecycle using my Enhanced Professional MLOps Lifecycle For A Predictive System and Component Models. It is a practical application of advanced engineering principles, specifically focusing on temporal data integrity, model governance, and decision-support systems rather than merely training a standalone algorithm.

Executive Overview

In the world of high-volume retail and SaaS, the Pareto Principle often holds true: 20% of customers generate 80% of the revenue.

The Problem: Most businesses cannot identify the top 20% until after they have already spent 12 months managing them.

The Solution: Capstone Project 1 solves this cold start problem. It is a production-grade predictive system that analyzes the first 30 days of a new customer’s behavior to accurately predict their value tier (High, Medium, or Low) for the subsequent 12 months. By bridging this prediction-decision gap, the system allows businesses to intelligently allocate expensive human resources (e.g., account managers) to high-potential clients while automating engagement for lower-tier segments.

Engineering Approach

Capstone Project 1 prioritizes reliability and economic utility (unlike standard academic projects that focus solely on accuracy).

  • The “Time-Wall” Architecture: To prevent the common error of data leakage, this project utilizes a custom SQL/Python pipeline that strictly isolates the observation window, which is day zero through day 30 (31 total days, including day zero), from the outcome window, which is day 31 through day 395. Thus, the outcome window is a complete calendar year of 365 days, but the total dataset is 396 days because of day zero.
  • Economic Optimization: Rather than being arbitrary, the model’s decision threshold is optimized using a cost-benefit matrix to prioritize precision, ensuring that expensive retention budgets aren’t wasted on false positive predictions.
  • Governance First: The system includes automated (1) drift detection to alert operators when consumer behavior changes and (2) fairness auditing to ensure demographic parity in predictions.

Available Documentation & Technical Portfolios

While this summary provides a high-level view, I will prepare specialized documentation suites tailored to specific professional interests. Please contact me or view the repository links for the set relevant to you:

NOTE: These documents will become available as I progress through my Enhanced Professional MLOps Lifecycle For A Predictive System and Component Models

1. For MLOps Architects & Engineering Leads

  • Focus: Technical Rigor, Reproducibility, and Leakage Prevention.
  • Includes:
    • The Mermaid.js Lifecycle Diagram (Phases 0-10).
    • The Leakage Audit Log proving Point-in-Time correctness.
    • The Data Lineage Map detailing the transformation from raw logs to feature store.

2. For Venture Capitalists & Investors

  • Focus: Scalability, ROI, and Business Defensibility.
  • Includes:
    • The Cost-Benefit Analysis showing reduced Customer Acquisition Cost (CAC) recovery time.
    • A Risk Mitigation Report on model compliance and governance assets.
    • Scalability Assessment for applying this architecture to Churn or Credit Risk domains.

3. For Prospective Consulting Clients

  • Focus: Reliability, Compliance, and Actionable Insights.
  • Includes:
    • The Model Card (Phase 8.2) detailing intended use and limitations.
    • Sample “Day 2” Dashboards showing how the model integrates into daily workflows.
    • SHAP Explainability Reports demonstrating how the model justifies its decisions to human operators.