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How to make a career switch into AI: 8 tips
How can you switch from another IT specialty into AI? Experts say there’s still time: Try these transition tips to start building an AI career path
Artificial intelligence (AI) in the enterprise is poised for significant growth, so it’s no wonder savvy IT professionals are looking for ways to align their career trajectories with it. Indeed, there was a 29 percent increase in the number of AI jobs listed on Indeed from May 2018 to May 2019, and a concurrent 15 decrease in candidate searches for AI roles, suggesting a potential shortage of AI experts on which IT pros could capitalize. (For more, see AI careers and salaries: 7 telling statistics).
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“Machine learning and AI has become ubiquitous in our lives,” says Timothy Havens, the William and Gloria Jackson Associate Professor of Computer Systems at Michigan Technological University’s College of Computing and Director of the Institute of Computing and Cybersystems. “This marketplace is going to grow exponentially in the coming years; it is the perfect time for both young people and also those who wish to make a career change to become involved in a field that is going to change everything.”
AI career path: How to start
For many IT professionals, a role in AI can be a logical next step that takes advantage of some of their existing skills. Programmers and developers who code in Python and R can relatively quickly upskill themselves to become data scientists, given the machine-learning libraries that are available out-of-the-box for those languages, says Zachary Jarvinen, head of technology strategy, AI, and analytics at OpenText.
“If you want to deploy your models, it makes sense to have good software engineering skills,” adds Vicki Boykis, who started out as a data analyst and now works in data science at CapTech Ventures. “And understanding the underpinnings of machine-learning libraries also requires knowing software engineering.”
The capabilities of IT architects and DevOps professionals lend themselves particularly well to data engineer roles, where they would be charged with creating and managing the large data pipelines that make machine learning possible, says Jarvinen. Even certain knowledge workers and domain experts – in information security, compliance, contracting, revenue ops, data capture, or operational efficiency – may seize opportunities to work on multi-functional AI project teams.
8 tips to transition to an AI career
How can you start preparing? Consider these steps if you are looking to transition to an AI-related role:
1. Solidify your software skills
“First and foremost, to be successful in a career in applied AI, one must be a strong programmer and all that entails: producing well-engineered code, debugging when problems occur, and strong data management skills,” Havens says. If you don’t know Python, learn it, adds Ram Palaniappan, senior practice director of data analytics & insights at TEKsystems.
2. Master AI 101
“It is important to develop a good understanding of the basics of machine learning and AI – the fundamental algorithms and types of problems these algorithms can solve,” Havens says.
3. Know your business
If you’re not already enmeshed in the domain in which you want to apply AI, get to know it well. “Learning what the business question is that needs to be answered with machine learning is probably the most important skill I’d emphasize,” says Havens. “This involves understanding the business, understanding how companies work in general, and listening skills.”
4. Seek out AI-oriented data science courses
An easy way to build the skills necessary for the machine-learning and AI field is continuing education. In addition to formal strong data science programs, there are also many viable, free options.