While the application of artificial intelligence (AI) technology is somewhat shrouded in a mist of ambiguity, few can deny the progress of its more grounded cousin, machine learning (ML).
Powering voice recognition, personalized recommendations and virtual assistants, the technology is becoming a part of our everyday lives. It’s creeping rapidly into every industry as businesses across the globe seize the opportunities to streamline processes and make products and services more engaging to customers. Machine learning is being used to process medical records
Carrying more than 20 years’ experience in his field, Dr. Greg Benson, a Professor of Computer Science at the University of San Francisco and Chief Scientist at SnapLogic, says the rise in ML adoption is creating a surge in demand for those equipped with the skills and experience to apply it.
“Machine learning is going to become a standard tool, part of the developer’s skill set,” Benson told TechHQ .
But don’t think that seasoned developers will be pushed aside by a new generation of ML-trained graduates. While ML is increasingly entering into the computer science curriculum, universities focus chiefly on encouraging a “growth mindset” over specific skills, and can lack the “real data, real problems” at the hands of tenured professionals, says Benson.
“New graduates are going to bring a lot to the table, but they’re going to have lots to learn from the older generation who are also, at the same time, going to be figuring out this machine learning ‘stuff’.”
With that in mind, here are four key considerations for developers with ambitions to pivot towards a career in ML as the technology gains further ground.
# 1 | Be goal-directed
Your first project doesn’t have to be super ambitious, says Benson, it can be very limited in skill required. The fact is, you will set yourself up for failure if you don’t set out with a specific reason to use the technology. Having a clear goal will guide and provide structure to your learning and your project’s (inevitably) unique requirements.
At the same time, if your CEO or leaderships requests that your IT department implement ML— not that they should— have an open conversation about where your company’s inefficiencies are or where you can save money or improve user experience, or “identify a domain or problem space, and work from there.”
“If you have a problem, everything that you read and follow is feeding into how everything you’re learning will solve a specific problem,” says Benson. “This is good advice for learning lots of things, but it’s particularly relevant for machine learning.”[…]