How to prepare for a career in machine learning and artificial intelligence
Thinking about pursuing a career in ML and AI? Here’s what you need to know.
Staying ahead of the tide is the mantra for today’s technology professionals. As technology and related processes evolve, those who work in the field must update their skills and even careers if necessary.
Some traditional help desk, system, and network administrator roles are fading out to be replaced by endeavors requiring a heftier and more diverse skills set. Machine learning (ML) and artificial intelligence (AL) are two such fields making steady inroads into the IT world. People looking for a future career in technology would do well to become familiar with both ML and AI.
I spoke to Dillon Erb, CEO of Paperspace, a cloud provider platform, to find out more.
Scott Matteson: What type of educational background is helpful in the ML/AI realm?
Dillon Erb: I heard an interesting stat recently: Approximately 70% of deep learning or AI practitioners today are still in school. Because this is an emerging technology, and it’s pulling in people from all sorts of disciplines, we don’t really have a great precedent for it yet.
Truth is, a majority of good practitioners in the space today are either self-taught, or they’re coming from a different domain entirely (i.e. not just computer science or programming). A solid background in statistics and traditional mathematics is always helpful — experience in a research area is also a big plus.
There are also many online courses like FastAI and Udacity — and myriad resources available from all the big tech players — to help educate yourself to become an AI developer. Being proficient on the data side is key and, in particular, Python, as it’s the primary language. Then on the other side, there’s the more traditional software architecture.
Generally today, we have a lot of people who are either really good at the software side and not so good on the mathematical or statistical side. Increasing or balancing your knowledge level and experience in both software and mathematics will give you a significant advantage in the job market.
Scott Matteson: How are traditional IT skills helpful?
Dillon Erb: The challenge IT faces today is that ML/AI is new. It’s a new type of tool that they have to learn about that didn’t exist before. All traditional IT skills are still very helpful and moving forward, there will be more focus on how machine learning tools stack with all the existing systems that are already deployed at an organization. But, right now, there are still questions around how to increase collaboration or visibility within an organization, and how to add more insight to all of the stakeholders. An IT department isn’t necessarily concerned with any single department, but making sure that a decision that’s made in one unit can be either moved to another area if it’s successful or adds a bird’s eye view visibility to the rest of the organization.
This is particularly relevant in the context of data science or machine learning. One of the concerns from IT today is that no best practices exist for AI/ML. The fear is that the practitioners are as siloed as the systems are across different departments, that these practitioners are not using version control for their model building or their software, and they struggle to keep these folks from operating in isolation.
I believe we’ll see a bigger demand for new machine learning tools to play better in a traditional IT context.
Scott Matteson: How are traditional IT skills not relevant?
Dillon Erb: I believe the machine learning universe will ultimately have to conform to a traditional IT process, more so than the other way around. The reason I say that is because IT has, at least in large organizations, broad initiatives like digitization, or collaboration, or very high-level initiatives around increasing developer velocity while still maintaining visibility to outside stakeholders.
