Data engineering  - Stack Overflow: Building the foundations of AI intelligence

This is a guest post for the Computer Weekly Developer Network written by Jody Bailey, chief product and technology officer at Stack Overflow.

Bailey writes in full as follows…

The demand for data engineers to build and scale cutting-edge AI models is expected to skyrocket as the excitement around its potential continues to surge. Despite plenty of discussion in recent years around AI replacing a vast number of jobs – experienced data engineers are crucial to developing intelligent AI models for the future.

Generative AI models require a vast amount of data pipelines to be developed and maintained. For data engineers, the challenges and expectations to develop robust AI models will also increase alongside demand, with AI rapidly becoming an extension of human intelligence and how we live right now.

With so much potential and expectation riding on our AI-centric future – just how important will the role of the AI data engineer become?

Structured & organised data

A crucial role for every AI data engineer is managing the data fed into the models in a structured format.

Neglecting to organise this data could see AI models fail before being fully developed, as the likelihood of producing inaccurate outputs increases.

Failing to ensure reliable data is at the heart of an AI model nullifies the model’s effectiveness.

When it comes to AI, the model can only be as good as the data it’s trained on. Essentially, structuring the input effectively is fundamental to the quality of the data that is produced.

Compliance at the centre of AI

Adhering to industry standards and regulatory requirements is very important to maintain stakeholder trust and protect sensitive information.

To ensure AI models can operate reliably and securely and reach their potential as knowledgeable partners, all data engineers must guarantee models comply with security and privacy regulations.

To ensure this happens, security and compliance standards must be placed at the centre of the development process.

Incorporating robust data governance procedures will reduce the likelihood of models developing into ones that contain bias or are not compliant. Building in line with industry standards from the outset is the only way to avoid potentially costly consequences of having to make last-minute adjustments.

Solving (software) socially

Developing AI models that produce balanced, verified and unbiased outcomes should always be at the forefront of a data engineer’s priorities. While AI’s omnipresence has grown in the last two years, so has uncertainty about the technology, even amongst developers. Stack Overflow’s latest Developer Survey found that 31% of developers remain sceptical about the accuracy of AI’s current capabilities.

Trust in the technology can be built as the inputs get more accurate, context-rich and attributable. Advocating for socially responsible AI is a fundamental step to building trust.

Scalable solutions

Stack Overflow’s Bailey: For AI to improve efficiency or business intelligence, data engineers must play a vital role in deliverying AI models.

The scalability aspect of artificial intelligence is one of the technology’s major benefits. As a result, data engineers who fail to manage the challenges of scaling AI models may limit the tool’s potential and effectiveness.

Scalability challenges can arise when a model becomes increasingly sophisticated and popular. Thankfully, tools are now available to support data engineers scaling AI models safely without compromising organised data. Knowledge graphs can support engineers in identifying complex relationships between datasets, making it easier to map unstructured data into structured sources.

Data engineers capable of securing data during the scaling step of the development process will bypass many of the challenges involved. Furthermore, these steps can provide the standard for future scalability of other, newer AI models.

Turning potential into reality

When building and maintaining a modern data platform, the pressure on data engineers from businesses can be immense. The key to success in this challenging environment isn’t to work even harder; it’s to be smarter about what you choose to work on.

Data engineers can play an important role in ensuring the rest of their IT organisation doesn’t fall into the habit of working in silos and that their engineering colleagues are familiar with or are educated about quality and governance standards, because the responsibility to incorporate those in at the end of the process can’t solely rest on data engineers.

If AI is to fulfil its potential of improving efficiency across public services or providing intelligent business solutions, data engineers will be a vital part of the entire delivery of AI models.