How organizations are sharpening their skills to better understand and use AI
Continuous learning is critical to business success, but providing employees with an easily accessible, results-driven solution they can access from wherever they are, whenever they need it, is no easy feat. Additionally, delivering valuable content in a variety of formats—whether that is through books, videos, or live online training—is crucial to supporting employees to upskill and reskill on the job.
According to Deloitte, evolving work demands and skills requirements are one big reason why continuous learning is critical, and there is no sector experiencing this more abruptly than technology. Executives and employees alike are worried about how emerging tech, such as robotics and AI, are changing jobs and how people should prepare for them.
In fact, a recent World Economic Forum report found that more than half (54%) of all employees will require significant reskilling and upskilling in just three years. So, what exactly are the skills data scientists and other tech titles are honing in response to this shift?
As the co-chair of the O'Reilly Artificial Intelligence conference, I regularly track broad changes in consumption patterns and preferences on our platform.
Python is the largest topic on our platform, and it also happens to be a popular language among data scientists (the second largest topic is another programming language, Java). Overall content usage, across all topics combined, grew by 8 percent from 2018 to 2019 (January to July). Among the fastest-growing topics are those central to building AI applications: machine learning (up 58 percent from 2018), data science (up 53 percent), data engineering (up 58 percent), and AI itself (up 52 percent).
One of the main reasons Python has been ascendant as a programming language is because of its popularity among data scientists and machine learning researchers and practitioners. In fact, of the top 20 most-consumed Python titles on O’Reilly Online Learning in 2019, several were focused mainly on data science and machine learning applications, including:
- Python for Data Analysis, 2nd Edition
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Python for Finance, 2nd Edition
- Machine Learning with Python Cookbook
In a survey we conducted earlier this year about AI adoption in the enterprise, respondents cited culture, organization, and lack of skilled people among the leading reasons holding back their adoption of AI technologies. As I noted in a recent article, adopting and sustaining AI and machine learning within a company will require retraining your entire organization.
To succeed in implementing and incorporating AI and machine learning technologies, companies need to take a more holistic approach toward retraining their workforces. The rapid growth in consumption of content in training-relevant topics on oreilly.com (including machine learning, data engineering, data science, and AI) provide early signs that companies and individuals are taking training seriously.
At our upcoming Artificial Intelligence conferences in San Jose and London, we have assembled a roster of two-day training sessions, tutorial sessions, and presentations to help individuals (across job roles and functions) sharpen their skills and understanding of AI and machine learning. In addition to our usual strong slate of technical training, tutorials, and talks, we return with a two-day Business Summit designed specifically for executives and business leaders.
Wholesale transformation will require cross-functional teams who are familiar with digital, data, and AI technologies. With this in mind, the AI conference in San Jose also will feature several outstanding new tutorials as well as executive briefings and case studies from leading companies and research organizations.
(This post originally appeared on the O'Reilly Media blog, which can be viewed here).