LinkedIn Learning

Most people now use ChatGPT, Claude, or Gemini every week without a clear mental model of what’s happening underneath. That’s fine right up until the model is confidently wrong and you have no way to tell. AI Model Basics is a new 20-minute LinkedIn Learning course aimed at exactly that gap. What the Course Covers How large language models actually work — enough mechanism to reason about them, without the math What prompting does and why it changes the output as much as it does Why models produce wrong answers, and what kinds of wrong to expect Personalization — what these tools can and can’t retain about you How to evaluate and improve what you get back instead of accepting the first response Who This Is For Anyone using AI tools at work who wants to understand them rather than just operate them. It’s beginner-level and assumes no technical background — no coding, no prior AI experience. ...

Most AI projects don’t fail at the model. They fail somewhere between the demo that impressed everyone and the system that has to survive contact with a real customer’s data, workflows, and definitions. I published a new LinkedIn Learning course on the role that closes that gap: Forward Deployed Engineering in the Age of AI. Why This Course Exists AI deployment differs fundamentally from a traditional SaaS rollout. A SaaS product is largely the same on day one for every customer. An AI system is not — its behavior depends on the customer’s data quality, their vocabulary, their edge cases, and their tolerance for being wrong. That means the work of making it succeed happens in the field, not at headquarters. ...

The world of AI engineering is moving incredibly fast. Every week brings new models, techniques, and breakthroughs. But beneath all that chaos, there are sophisticated patterns and architectural principles that remain consistent across implementations. I recently published a new LinkedIn Learning course: Fundamentals of AI Engineering: Principles and Practical Applications. Why This Course Matters After building mission-critical systems at companies like Palantir and Citadel, I’ve learned that the gap between AI research and production-ready systems is often wider than expected. This course bridges that gap by focusing on the engineering fundamentals that actually matter in production environments. ...

Managing SQL code at scale is one of the biggest challenges in data engineering. As data teams grow and pipelines become more complex, traditional approaches to SQL development quickly become unwieldy. This LinkedIn Learning course explores how dbt (data build tool) transforms the way we think about SQL development, bringing software engineering best practices to analytics engineering. Course Approach Real-World Problem Solving: Each chapter presents actual situations and challenges that data engineers face, with focused code examples showing practical solutions. ...

Originally streamed live on August 3, 2023 - LinkedIn Learning’s “The Future in Tech” series Data engineering is the unsung hero fueling the rapid growth and consumption of artificial intelligence. It transforms AI’s potential into reality, driving digital innovation and reshaping the world. In this comprehensive discussion, we explore how data engineering unlocks and enables democratized use of Artificial Intelligence. Video: The Future in Tech - Data Engineering and AI Discussion (1,668 views) ...