The Data Scientist’s role has become a critical function in businesses across all industries, partly because it’s a role focused on model development, allowing businesses to develop the best model possible for a use case. As technology has evolved, so have the businesses' needs as they adopt the latest innovations – and ultimately, the roles needed to steer the ship. Businesses can now benefit from having an AI (opens in new tab) Engineer on their IT management (opens in new tab) team, complementing their Data Scientists to push projects further than before while providing an evolved approach to tasks.
Sumant Kumar is Director of Digital Transformation at CGI (opens in new tab) in the UK.
While there are benefits of both roles, there are also some key differences. The AI Engineer is more focused on making models that are a production solution in line with software engineer practices such as DevOps (opens in new tab), APIs and Cloud (opens in new tab). Alongside this, the AI Engineer may work more closely with other areas of the business, meaning they are better positioned to ensure the platform being developed meets requirements across the board. On the other hand, a Data Scientist is often more siloed in the way they work, as their role often requires them to focus solely on the details of the project and less so about how it interacts with the world beyond it.
The AI engineer is something we at CGI have set up internally, and it’s a role we think other businesses should adopt should they want to take projects from experimentation to production and increase cross-departmental collaboration (opens in new tab) and innovation.
What is an AI engineer?
As this is a new role and concept for many, it’s probably helpful to highlight a few of the key attributes the role has, its expectations and responsibilities.
The new role involves a lot of study and analysis of problems. As such, the person who takes up the position should be comfortable proposing solutions and designing objectives that marry business objectives. All of which will aid in the development of models and the achievement of associated metrics.
Alongside all of this, they are also responsible for deploying, running, and monitoring model performance, exploring data and data visualization, identifying how differences in data (opens in new tab) distribution can affect performance when deploying these models in the real world.
Effectively, the AI Engineer is responsible for the entire project from inception to deployment, and it’s through the careful navigation of testing, review and meeting with others from the business to make sure that any potential paint points are addressed as early on as possible.
Experimentation to production
With a better understanding of what the AI Engineer is, we can now begin to look at what the role brings to a business.
Perhaps the main benefit this role brings, along with the aforementioned connection to the rest of the business, is that it puts someone in charge of building a common standard around the platform. By having a person in control of your AI projects, they have the oversight to truly understand how the project is likely to develop, where it needs to be altered and tested further, and how it is ultimately brought to production.
Too often, projects with extensive time and money already invested in them don’t make it to production. It’s understandable, and I’d never call a project that doesn’t make it to production a failure; rather, it’s an opportunity to learn and develop more robust plans and projects for the future. That being said, avoiding the issues that cause projects to be axed is far more useful. Having an AI Engineer on your team is an extra line of defense to identify potential problems and hurdles and overcome them without impact the project as a whole.
It can’t be emphasized enough that the AI Engineer’s further benefit is the potential elimination of siloes within your business. More and more often, a project intersects with several departments within an organization, and yet these departments still don’t communicate and work as closely together as would be ideal.
The AI Engineer is better positioned than others, such as the Data Scientist, which can be a little more insular, to work with colleagues across the business and ensure that projects are better positioned to meet necessary business requirements. However, we can see that both are necessary and bring their own benefits to projects.
The AI Engineering team works to establish an Enterprise AI Platform model approach while Data Scientists develop machine learning (opens in new tab) models using a consistent approach and best practice standards. All of which helps to avoid developing silos while driving reusability by developing machine learning solutions that are task component level to allow composability/reusability.
It is imperative that businesses avoid siloes as much as possible. We live in a world where business is becoming increasingly connected, and borders are blurring due to globalization. As this evolves, we also see the traditional borders within a business begin to blur as departments work more closely together to create more innovative products and provide a more robust service to customers.
Overall, by adopting the AI Engineer, businesses will create more robust projects gaining greater control in a tempestuous and unpredictable world. By having someone who acts both as a bridge, developer and project manager, you can help ensure that time, money and efforts are invested wisely at all stages of a project, making the AI Engineer a critical and important asset.
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