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Today, everyone wants Artificial Intelligence. it’s finding its way into everyday business and government. The idea of AI is not a new, but what is different is that today’s hardware and software is bringing the various concepts underpinning AI to a mass market.
So how do you go about setting up a team capable of turning raw data into new technology? What are the skills and types of people you need and where do you find them? And does your approach differ from software engineering projects? It is tricky, when you consider there are more openings in the AI than people to place them.
When it comes to language, perhaps this is the easiest box to tick. Increasingly, R and Python tend to be the most commonly used programming languages in this area. Looking at a more hardware-optimized path, going down to the GPU? Then skills in C/C++ could be the ticket.
But data is where things get tricky, which is a challenge as AI is predicated on Machine Learning and ML eats data.
Once a niche research specialty, AI is fast becoming a vital aspect of the IT strategy at many businesses. The maturity of Data Science and Machine Learning tools, as well as the rise of readily accessible ML platforms in the cloud, are fuelling this trend, enabling businesses to explore new ways to extract high business value from existing and accumulating data.
What successful AI looks like
When first building an AI department, it is important to know that successful AI requires multiple roles with differing skillsets. We found that an AI group needs at least three distinct roles: a data engineer to organise the data, a data scientist to investigate the data and a software engineer to implement applications.
Recruiting and retaining AI talent
AI professionals are in high demand. To assemble and maintain an AI team, retention and recruitment are key. But that doesn’t necessarily mean having to look outside the organisation.
There is Not One Magic AI Hire
The description of skills needed for managing the adoption of AI and Machine Learning sound astoundingly like those associated with the Data Scientist. The thought was that a Data Scientist needs to understand the analytics layer, the implementation and the business. After a mad rush to try to hire individuals who encapsulated all of these skills, enterprises realized that putting together teams that included these skills made more sense. The trick in building these teams is to make sure that they reflect the actual work that needs to be done. In particular, the trick is in understanding that the sophisticated technical expertise, while it is crucial, is only part of the equation.
It Takes a Team
As we look at the skills needed to bring AI into the enterprise, we need to address the same problem: the need for data wranglers. But, because many AI systems are going to be used by non-technical users, the ability to handle data issues is only part of the problem when we consider things from a product point of view. We are going to need teams that encapsulate skills that touch on the entire flow, from business problem to deployed product.
In a perfect world, a team should include elements from the business, practical AI, UI/UX and developers up and down the stack. They should be linked together through a shared problem description and managed by a technical product manager.
To achieve that, we had to create a diverse team. We generally believe it is important to have diverse teams and interdisciplinary skills in all of our software development teams. We were looking for few different skill sets:
• Software engineers that are interested in Data Science to help build the platform. Software developers are very important because they can introduce the team to good software practices, both for the development and the later deployment of the solutions.
• Data engineers that could help the team effectively work with the data. Preparing data sets and feature lists is an important part of every ML project, and having people with such expertise makes it a much more efficient and less frustrating process.
• Statisticians and mathematicians that would help the team understand data and theoretical aspects. This is an important part of being sure that the solution makes sense for the data that we have; it is also a big part of the exploratory analysis. Secondly, mathematicians often have a wide knowledge of various algorithms and approaches to many common problems. They also deeply understand how everything works under the hood.
• Business experts: these are not necessary domain experts, but people that can work with stakeholders to understand their goals, and help them understand the approach. Great communication skills are very important for that.