Here’s a guide to finding the right resources for your AI teams for maximum output.
Here’s a guide to staff your Artificial intelligence – seems like there is no stopping it. The word echoes in the tech industry with more companies embracing this disruptive technology. There are millions of articles on the web and many on our website which talk in-depth about how revolutionary AI is. The sheer diversity and complexity of AI projects at times like now where rapid production is required to meet the pandemic demands are creating a need to identify key AI roles and finding the right personnel for the job.
What is the challenge?
Organizations face challenges in leveraging artificial intelligence projects due to the lack of required skills, collaboration, tools, and the ability to create and manage a dynamic, production-level AI pipeline. According to Gartner, by 2023, 50% of IT leaders will find it tough to move their artificial intelligence products past proof of concept (POC) to a production-grade maturity. This is a high failure rate, and to counter this companies need to create the right AI roles for success.
“In many organizations, data scientists are still wearing too many hats due to a death of talent across other roles,” said Arun Chandrasekaran, VP Analyst at Gartner.
It’s a team effort
To successfully run the operations and leverage artificial intelligence initiatives, companies need to create diverse AI roles and skills.
“Artificial intelligence is a team sport. On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with artificial intelligence architects and machine learning engineers. Together they can envision, build, deploy, and operationalize an end-to-end machine learning / artificial intelligence pipeline.” said Arun.
Artificial intelligence teams in any business setup should not operate in isolation. The teams need to collaborate with domain experts, IT experts, and other necessary staff and stakeholders to achieve results that successfully drive AI initiatives.
The right resources
For the success of artificial intelligence projects, finding the right resources and making sure they work in alignment with other business processes is crucial. Two roles that are significant to get the desired result are AI architects and machine learning engineers.
The artificial intelligence architect is solely focused on the transformational architectural efforts that AI technology introduces. Their main job role is to orchestrate the deployment and management of systems in production and provide inputs on the capability of machine learning and deep learning models within artificial intelligence’s many disciplines like NLP (natural language processes) or image recognition.
Machine learning is one of the most used branches of AI. Hence, organizations are increasingly hiring machine learning engineers as a part of their artificial intelligence teams. These professionals are responsible for moving machine learning solutions in production and optimize the environment for maximum performance and scalability.
According to Gartner, by 2023, the role of a machine learning engineer will become one of the fastest-growing roles in the AI industry. Gartner also estimates that in today’s date, there is one machine learning engineer for every 10 data scientists and it will likely increase to 5 and 10 by 2023.
“ML engineers need to ensure that AI platforms deliver against technical and business SLA requirements. ML engineers are expected to be the connecting fabric with data scientists from an IT perspective and ensure their ML models run well in production”, said Arun to Gartner.