| |SEPTEMBER 202219CIOReviewCXO INSIGHTSBefore we talk about the machine learning space, can we have a brief about you?My background is in data science and machine learning (ML) engineering, and I'm currently handling machine learning engineering and operations at Volvo Cars. Before joining Volvo Cars, I worked across multiple industries. This experience exposed me to numerous problems within the ML space, and I found synergies and similarities in terms of the issues different businesses have and the prevalent pain points. Once I joined Volvo Cars, I moved toward operationalizing ML technology in the current context. My main task here is to set up a dedicated infrastructure for machine learning and find common features of ML systems with applications across different products. So, in light of your experience, what are some of the trends or challenges you have witnessed in the ML space today?I think the main challenges right now are not actually technical; rather they are mainly cultural. Also, I feel there's a trend when AI and ML became this huge buzzword, and everybody just wanted to jump onboard and magically get a lot of value out of ML and AI. They sprinkled the data scientists across different units and domains of their respective organizations, and they eventually became siloed. But now we realize that data scientists or ML engineers alone don't really have the capability to operationalize machine learning systems and maintain them over time. Because most Data Scientists have their background in academics and the theoretical side of the technology, they lack the real-life business context and engineering practices to build productionized ML products. In this regard, building cross-functional teams that can collaborate with each other is one of the major organizational challenges apart from the cultural challenge.Besides, machine learning development inherits all the challenges of software development. Therefore, getting a machine learning system to production means businesses need THE ROLE OF AI AND ML IN OUR DIGITAL FUTUREBy Leonard Aukea, Head of Machine Learning Engineering and Operations, Volvo CarsBuilding cross-functional teams that can collaborate with each other is one of the major organizational challenges apart from the cultural challengeto treat it like software. But there are additional challenges explicitly related to machine learning due to the algorithms being stochastic in their nature, so you have to accept some margin of error in your results. This is also something that one must clarify when communicating with stakeholders and actual users of ML technology.So, when it comes to your organization, are there any trends you are leveraging in-house to seamlessly provide machine learning capabilities to your clients?We are working heavily on adopting MLOps, philosophies, and principles to streamline ML development and empower different ML teams across several domains. First, we are conducting educational sessions and building a foundation of organizational best practices. We are also developing a central team for maintaining and operating the ML infrastructure. For them, we have abstracted away certain services into standard APIs that can be easily used and accessed by these particular teams. We're also pushing them to maintain and care about system design so that they don't acquire too much technical debt over time. This allows us to have a central cross-functional team comprising MLOps, operations engineers, data scientists, and AI product managers. This enables us to streamline and deliver end-to-end ML products.How do you envision the ML space over the next 12 to 18 months? Is there any piece of advice that you want to give to the upcoming professionals in the field?I envision it being even closer to software engineering, and I feel that this transformation is currently ongoing. In essence, the way we build ML products will resemble more and more the way we build software products. And in the ML field, building a good foundation in terms of software is necessary. It will make businesses way more effective and increase the probability of getting to the production stream; otherwise, the castle will crumble.
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