AI Engineering 101 : What It Is and How It Differs from Other Disciplines
Explore AI Engineering, an emerging discipline that focus on building applications with pre-trained AI models.
What is AI Engineering?
AI Engineering is an emerging discipline that focuses on building applications and solutions using pre-existing, powerful AI models. Unlike traditional software engineering or machine learning engineering, AI Engineering leverages foundation models – large, pre-trained AI models capable of a wide range of tasks – to create practical, real-world applications.
Key aspects of AI Engineering include:
Adapting foundation models for specific tasks
Integrating AI capabilities into applications
Optimizing AI performance for production environments
Evaluating and fine-tuning AI models for specific use cases
AI Engineering has gained prominence due to the availability of powerful models like GPT-4o (OpenAI), Claude 3.5 Sonnet (Anthropic), Gemini 1.5 Pro (Google), Llama 3.2(Meta) and others, which can be accessed through APIs and integrated into various applications.
“AI Engineering focuses on building applications using pre-existing, powerful AI models like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.2.
AI Engineering, ML Engineering, and Software Engineering
AI Engineering combines elements from both Software Engineering and ML Engineering, but with a unique focus on adapting and applying pre-trained AI models. While an AI Engineer may not need the deep mathematical knowledge required for ML model development, they do need to understand how to effectively use and adapt these models within software applications.
This interdisciplinary nature of AI Engineering makes it an exciting and accessible field for those with backgrounds in either software development or data science. It allows for the rapid development of AI-powered applications without the need for extensive machine learning expertise or the resources required to train large models from scratch.
To better understand the role of an AI Engineer, it's helpful to compare it with related fields: ML Engineering and Software Engineering. The following Venn diagram illustrates the similarities and differences among these disciplines:
ML Engineering vs AI Engineering
While ML Engineering and AI Engineering share some similarities, there are key differences:
Software Engineering vs AI Engineering
While AI Engineering builds upon traditional software engineering principles, it introduces new concepts and challenges:
In conclusion, AI Engineering represents a paradigm shift in how we build software applications. It combines elements of software engineering, machine learning, and emerging AI technologies to create powerful, adaptive applications. As we progress through this guide, you'll gain insights into the AI Engineering lifecycle, essential knowledge and skills, and practical steps to start your journey in this exciting field.