From the course: Fundamentals of AI Engineering: Principles and Practical Applications

What is AI engineering?

From the course: Fundamentals of AI Engineering: Principles and Practical Applications

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What is AI engineering?

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- [Narrator] AI engineering is where traditional software engineering meets artificial intelligence. It's not just about adding AI to existing systems. It's about rethinking how we build software entirely. Traditional software engineers work with deterministic systems. Given the same input, you always get the same output. Write an IF statement, and behaves exactly as you coded it. AI engineering is different. We work with probabilistic systems, meaning given the same input, you might get slightly different outputs each time. This fundamentally changes how we design, build, and test our systems. Let me highlight three key differences that make AI engineering unique. First, data is your code. In traditional software engineering, you write logic to handle each case. In AI engineering, you often use data to teach the system how to handle cases. The quality of your system depends just as much on your data as your code. Second, testing is different. You can't just write unit tests that check for exact matches. Instead, you need to think about accuracy ranges, edge cases, potential biases, and many other options. A 98% system might actually be worse than a 95% accurate one if it's biased against certain user groups. Third, deployment isn't just about shipping code. You need to start thinking about model versioning, monitoring for data drift, and handling much larger computational resources. What does this mean in practice? As an AI engineer, you'll spend your time building data processing pipelines, integrating language models and other AI components, optimizing system performance and costs, monitoring model behavior and production, debugging, not just code, but also model outputs, and making architectural decisions about AI components. You're the bridge between data scientists and ML engineers who work on models and the production system where these models need to perform reliably at scale. This skillset is in massive demand across the software industry today. Companies are rapidly transitioning from just exploring AI to needing engineers who can build reliable, scalable AI systems. The gap between proof of concept and production-ready AI is where AI engineers thrive. In this course, we'll focus on building advanced retrieval systems and working with large language models, not because these represent the entirety of AI engineering, but because they exemplify the core challenges found across the field. These systems require sophisticated architecture with multiple components working together, careful optimization and rigorous evaluation. The concepts you'll master, working with probabilistic systems, designing multi-stage pipelines, optimizing for competing constraints, implementing robust evaluation, and many others applied universally across AI engineering domains, from computer vision to recommendation systems to natural language processing. The tools we'll use reflect this unique role. We'll work with LLM deconstruction, specifically working with local LLMs, document processing libraries with unstructured data, vector databases, language models for text understanding and generation, and retrievers by retrieving documents from an ecosystem. Throughout this course, we'll build skills step by step. By the end, you'll have the fundamentals to build production-ready AI systems, not just RAG applications, but any AI-powered solution your project may need. Get ready and here we go.

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