As Artificial Intelligence (AI) technologies become more mainstream in the enterprise, what skills set you apart? Individuals and IT teams, focus on these four
Artificial Intelligence (AI) has arguably become a household term in modern enterprises. By now, most companies have embraced some type of business initiative that includes AI in their digital transformation.
Artificial Intelligence is a broad term, but much current research and development focuses on machine learning (ML), a subdiscipline whereby machines learn from data as opposed to being explicitly programmed.
AI skills to watch
With AI and ML targeting a broad spectrum of enterprise users, IT professionals must develop new skills to succeed in this emerging space. Here are four examples.
The essential question is whether such data has the potential to solve the business problem at hand. While the answer is not always immediately obvious, it begins with a hypothesis stemming from prior analysis or perhaps simply based on intuition. For example, a business experiencing high customer churn might hypothesize that recent changes in commercial activity could predict future attrition.
Increasingly, enterprises are realizing the importance of building systems and processes that automate the acquisition, transformation, and delivery of data to organizations involved in analytics and AI/ML projects. These enterprises understand that data should be a first-class asset on par with code and that the core principles of software engineering should be applied in a similar fashion.
While all IT professionals should have basic data transformation skills, we will likely see the emergence of centralized data engineering teams whose primary purpose is to develop and deploy automated data pipelines that deliver high-quality data at scale.
Various programming languages are used for machine learning, but Python is the most common. Much of its success is due to an active and vibrant community as well as the availability of libraries that implement virtually all the popular algorithms.
The differentiation of skills between data scientists and software engineers has blurred in recent years due to advances and accessibility in tooling. A project that might once have required a data scientist may now be done by IT professionals.
A project that might once have required a data scientist may now be done by IT professionals.
Even as these tools become more advanced, IT professionals should have a general understanding of machine learning concepts – particularly in evaluating model performance and correlating feature selection with predictive quality.
Leveraging artificial intelligence and machine learning to improve business outcomes is quickly becoming table stakes for modern enterprises as they navigate digital transformation initiatives. Embracing these evolving technologies requires IT organizations to develop new skills aimed at using data to solve business problems. To better enable organizations engaged in AI projects, IT teams should also implement new systems and processes that automate the acquisition, transformation, and delivery of data.
A variety of resources are available online to help IT professionals gain the AI and ML skills they need. Coursera.org offers an excellent introductory course that teaches the fundamentals of machine learning. Additionally, all major cloud providers, including AWS, Azure, and Google, offer training for their AI services and integrated toolchains. While many of these online courses are free, some – such as certification programs – involve a fee.