The most important skills for successful AI deployments
With artificial intelligence (AI) deployments carrying many risks if not deployed properly, we explore the most important skills for workforces to have
Just as technologies evolve and become more useful in the enterprise, so do the skills needed to deploy them successfully, and AI is no exception. For software development in general, the importance of formal technical education is waning, as found by a Codingame report, which revealed 80% of HR professionals to have hired programmers who were self-taught.
“When we think of deploying AI in the enterprise at scale, the skills that are needed are evolving,” said Beatriz Sanz-Saiz, global data and analytics lead at EY. “The skills needed to obtain a PhD in the field, for example, are no longer necessary.
“Companies need a foundation of AI engineers that can not only manage the algorithms, but also the data involved.
“Companies will need increasing numbers of data engineers and data skills to shape modern architectures. Without those skills, it will be very difficult to bring in AI at scale.”
An ability to manage and analyse masses of data, as well as a willingness to learn quickly and clearly communicate with colleagues across the enterprise, are seen as more vital in today’s world than exactly how digital skills were obtained.
Traditionally, AI development has been thought of as a model creation process, which ends once the model has been created. However, deploying this technology now requires a range of other aspects. With AI deployment needing multiple datasets, one of the most vital pieces of this puzzle is compliance.
“What is becoming clear is that in the model’s entire lifecycle, training is only the start of it, so the skills needed go beyond data science capabilities,” said Alejandro Saucedo, engineering director of machine learning at Seldon.
“The IT and compliance requirements are now just as critical to the process. Then you need to consider the operational components that are brought in, depending on the use case. Compliance checks call for roles such as operational managers, delivery managers and domain managers.
“Ultimately, the AI skills that are needed today boil down to data science capabilities, software engineering capabilities, IT operation capabilities, and domain expertise.”
Dr Iain Brown, head of data science for UK & Ireland at SAS, has seen the evolution of skills needed to deploy AI first hand.
He said: “I’ve been in this industry around 15 years. I have a statistics background, and picked up the computer science elements over the years, but I was very much focused on the analytical side of things.
“What organisations really need more of now is those at the top and tail of the process. This means DevOps, procuring the right environments and putting in the infrastructure for these models to be developed, and then ModelOps, where those models are being taken through a process and deployed into production environments.”
It’s within those end results of the deployment process where monitoring, governance and validation of AI models need to be considered. These aspects, alongside the ability to find and introduce the most fitting infrastructure for the process, have proven as equally necessary as maths, statistics and computer science.
Brown believes that DevOps and ModelOps competencies have been especially successful within the banking sector. Here, larger organisations have been leveraging a combined view of business problems, identifying them and adapting the modelling ecosystem accordingly.
