Digital Economy Dispatch #205 -- What is AI Engineering?

Digital Economy Dispatch #205 — What is AI Engineering?
13th October 2024

As more and more organizations look to adopt AI to accelerate their digital transformation activities, two key trends are emerging. In the first, AI is being increasingly used by software engineers to assist with a wide variety of coding practices, from the way software is created to how it is tested and deployed. In the second, AI capabilities are being embedded within the processes to create new systems and services, requiring new kinds of approaches to designing and delivering them.

This shift has been described as the emergence of a new discipline at the intersection of AI and software development: AI Engineering. But what exactly is AI Engineering, and how does it differ from traditional software engineering?

Defining AI Engineering

Broadly defined, AI Engineering focuses on developing tools, systems, and processes to enable the application of AI in real-world contexts. While it shares some similarities with established approaches for delivering large-scale software systems, the rapid advances in AI are opening up new possibilities for how such solutions are developed, deployed, and evolved.

As a result, AI Engineering encompasses two distinct concerns:

1. The practice of constructing and maintaining software with increasing support from AI tools and capabilities.

2. The processes of designing and delivering AI-based systems.

AI Engineering vs. Traditional Software Engineering

To better understand AI Engineering, let’s compare it with traditional software engineering across four critical dimensions: role, change dynamics, technology stack, and skills.

Role: From Logic Architects to Data Orchestrators

In traditional software engineering, the primary role has been to translate business requirements into logical, deterministic sequences of instructions. Software engineers typically work within a structured framework, following established methodologies like waterfall or agile. Their focus is on coding, testing, and debugging, ensuring the reliability and efficiency of the software.

AI Engineering, on the other hand, extends beyond traditional software development. AI engineers act as data orchestrators and model architects. Instead of writing explicit instructions, they focus on curating data, selecting and fine-tuning models, and managing the probabilistic nature of AI outputs. Their task is to develop algorithms, train models, and integrate AI capabilities into existing software applications, enabling machines to perform tasks that were previously the exclusive domain of humans.

Change Dynamics: Predictable Evolution vs. Continuous Learning

One of the most striking differences in the AI era is the approach to change management. Traditional software engineering changes are planned, version-controlled, and follow a predictable pattern of development, testing, and deployment. While agile methods have introduced more flexibility, the overall approach remains relatively structured.

AI Engineering, however, is fundamentally iterative and experimental. AI models are continuously refined and improved based on feedback and new data. This focus on agile approaches from the outset requires a mindset that embraces uncertainty and is willing to adapt to changing circumstances. The systems being built not only evolve over time as they encounter new data, but they also require a view of development as an ongoing process of model monitoring, retraining, and performance optimization.

Technology Stack: From Monolithic to Modular AI Infrastructure

While programming languages like Python, Java, and C++ remain essential in both disciplines, AI engineers must also be proficient in specialized ML tools and frameworks such as TensorFlow, PyTorch, and Keras. These tools provide the necessary infrastructure for building, training, and deploying AI models.

The technology stack for AI Engineering can differ significantly from traditional software engineering:

• Primary Tools: While software engineers use IDEs, version control, and testing frameworks, AI engineers rely on Jupyter notebooks, ML frameworks, and cloud GPU platforms.

• Infrastructure: Traditional application servers and databases are replaced by model serving platforms and vector databases in AI Engineering.

• Deployment Focus: Software engineering focuses on application binaries and containers, while AI Engineering deals with model weights and inference endpoints.

• Monitoring: Error logs and performance metrics give way to model drift and prediction quality monitoring in AI systems.

• Testing Approach: Unit tests and integration tests are supplemented by data validation and model evaluation in AI Engineering.

• Scalability Concerns: While software engineering worries about concurrent users and transaction volume, AI Engineering focuses on training time and inference latency.

Skills: Expanding the Engineering Toolbox

Beyond technical proficiency, AI engineers require a unique set of skills that go beyond traditional software development. They need a deep understanding of probability, statistical modelling, and the mathematical foundations of machine learning. Proficiency in data pre-processing, feature engineering, and managing large-scale datasets is crucial. AI Engineering requires sophisticated A/B testing and the ability to design robust experiments for model evaluation.

Perhaps most importantly, AI engineers must actively consider and mitigate potential biases in ML models, demanding a new level of ethical consideration in their engineering practices. This focus on ethics and bias awareness is a critical differentiator in the field of AI Engineering.

From Software Engineering to AI Engineering

The emergence of AI Engineering represents a paradigm shift in how we conceptualize, develop, and deploy digital solutions. While the foundational principles of software engineering remain essential, AI Engineering demands that we expand our perspective and capabilities.

According to Gartner analysts, they expect AI will expand its impact on the software engineer’s role in substantial ways. In the short term, AI tools is used to automate several core programming tasks to provide modest productivity increases by augmenting existing developer work patterns. However, going forward Garter suggest that the emergence of AI agents will push the boundaries of how AI is used by software engineers.

In the medium term, Gartner believe that this will enable developers to automate and offload more tasks to the point that most code will be AI-generated rather than human-authored. While in the longer term, they foresee advances in AI that will mean that organizations will require software engineers with substantially different skills and an AI-first mindset to meet the rapidly increasing demand for AI-based solutions.

This move from software engineering to AI engineering presents both opportunities and challenges. It’s a fundamental shift in how organizations approach problem-solving in the digital world. By understanding and embracing these changes, we can better position our organizations to leverage the power of AI while maintaining the rigorous engineering practices that have served us well in the past.

As digital leaders, recognizing and embracing this transformation is crucial. It requires changes in organizational structure, development processes, and strategic planning. The future belongs to those who can effectively bridge the worlds of traditional software engineering and AI systems, combining precision and reliability with adaptability and learning capabilities.

Understanding AI Engineering is not just about staying current with technological trends; it’s about positioning your organization to leverage the full power of AI while maintaining rigorous engineering practices. As we navigate this transition, our success will depend on our ability to evolve our practices, our teams, and ultimately, our understanding of what it means to be an engineer in the age of AI.

As we move forward, the distinction between traditional software engineering and AI engineering may blur, with AI-first approaches becoming the norm rather than the exception. Organizations that can effectively navigate this transition, adapting their practices and cultivating the necessary skills, will be well-positioned to thrive in this new era of digital innovation in the age of AI.