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Research from STX Next, Europe’s largest software development company specialising in the Python programming language, has found that 29% of developers have completed courses in AI and machine learning (ML), making it the third-most popular area for training behind web development (67%) and software development (65%).
Despite this, only 6% expect to see more AI or ML tools and functionality in future versions of Python, underlining how more needs to be done to support software developers in their current and future AI and ML projects.
The findings were taken from STX Next’s 2023 Python Tech Radar report, which collected insights from a range of Python developers. Other key figures include:
• 17% of respondents see AI, ML, data engineering and data science as the next big things in Python, ranking higher than any other area, including performance (15%)
• 36% are undecided on what the next big things in Python will be, but are curious to find out
• 25% want to enhance their AI and ML skills, only ranking behind more established areas such as software architecture (76%), software development (52%) and leadership (28%)
Ronald Binkofski, CEO at STX Next, said: “Stories about AI are dominating the news schedule at the moment, with much of the debate focusing on the future of the technology and whether regulation is needed to keep its progress under control.
“In any case, AI and ML are here to stay; there is a strong appetite among software developers for building and growing their skills in this area, and they recognise its potential in helping them complete more innovative and ambitious projects.
“However, there is still some ground to make up to ensure that developers have the tools at their disposal to elevate their skills to a new level. Despite Python’s status as the go-to programming language for AI research and the practical application of ML and data science, it still has room for improvement in terms of functionality.”
“To meet this demand, future updates to Python – and other popular programming languages – should focus on implementing improved AI and ML tools as a matter of course. Updates to other areas such as performance and security are always crucial, but making it easier to carry out AI-related projects has to be a priority from now on as well.”
Binkofski concluded: “On a more general level, it’s also vital that developers are given the training they need to deepen their AI and ML skills, outside of the programming process itself. If organisations emphasise holistic training for their employees in this area – covering everything from wider AI-related concepts, all the way through to the intricacies of coding for AI – they will set themselves up for a successful future.”
