Скачать или смотреть Building a Machine Learning Model to Assess Candidate Skills at Scale

In this project, I present a machine learning solution designed to assess the skills of job candidates at scale. Traditional hiring methods rely heavily on resumes and interviews, which are often subjective and inconsistent. With the rise of skill-based roles and AI-assisted work, there is a growing need for scalable, data-driven, and fair methods to evaluate real capabilities.

This video walks through the complete ML project lifecycle — from identifying the opportunity and defining the business problem, to selecting an appropriate system design, outlining experimentation steps, and considering production risks like data drift and concept drift.

🔍 Key Highlights:
Opportunity evaluation and business case
Model concept and feature selection
Iterative experimentation plan
ML system design decisions (cloud vs edge, batch vs online, etc.)
Potential production risks and mitigation strategies

The goal of this project is to enable organizations to make faster, fairer, and more accurate hiring decisions using machine learning.