AI Upskilling Framework: Level 4 Training and Maintaining Models

A framework for AI upskilling by LinkedIn Learning Team.

In this new era of work, AI skills will be critical for nearly every role. But the level of AI readiness will vary: An entry-level sales representative, seasoned marketing professional, data analyst, and engineer will all need different skills to incorporate AI into their day-to-day work. The framework is organized into five levels of AI expertise. The first two levels contain foundational AI knowledge that all employees will need, while the top three levels require deep technical skills and specialized expertise: level 3 is designed for business power users, developers, and data engineers; level 4, for machine learning engineers; and level 5, for cloud specialists, cybersecurity professionals, data scientists, researchers, and those preparing for tech certifications.

AI Upskilling Framework Community Serie by the Global AI Community will happen every Monday for next 5 weeks:
📌 Understanding AI – July 14
📌 Applying AI – July 21
📌 Building AI Solutions – July 28
📌 Training & Maintaining Models – August 4
📌 Deeply Specializing – August 11

In fourth session, Yimi Wang, Jose Luis Latorre and Dr. Alexander Wachtel will talk about the Level 4: Training and Maintaining Models!

While machine learning engineers have long used AI to build software and develop and train AI models, the momentum and speed of change to AI-driven software requires frequent upskilling and reskilling to help them stay on track. This pace of change only exacerbates an existing issue: Talent with deep technical expertise is both harder to find and more expensive to hire. IDC predicts a global shortfall of 4 million developers by 2025. This looming talent gap makes upskilling your technical talent mission critical.

Level 4 of the framework focuses on the skills needed by employees in engineering and coding-heavy roles who are building AI systems and products. Topics include deep learning and neural networks, as well as training, maintaining, and fine-tuning AI models.

L&D is well positioned to help these engineering leaders upskill their teams — both current employees and those new to the company — so they are better equipped to quickly and effectively deliver the AI-powered applications that the business needs.

Please join us!