MLOps is a new and evolving area full of challenges and possibilities. It is new enough that it is unclear exactly what it will look like in a few years as more engineers and companies start to implement it.
In order to get some insight into who will be responsible for shaping MLOps I created a Reddit poll in the r/mlops subreddit. The current distribution of roles and backgrounds in the community should help to show where MLOps is gaining traction the fastest and where it is lagging.
A Reddit Poll in r/mlops
I draw three major conclusions from these results:
- Data Scientists, Data Engineers, and AI/ML Engineers are the most engaged in MLOps right now. I interpret this to mean that data teams are seeing a rapid need to close the gap between traditional data science activities and delivering, operating, and improving machine learning products.
- The community consists largely of experienced professionals and has less momentum with students and new grads. This is likely due to MLOps being driven from a need within the software industry.
- Perhaps most surprisingly is not many people from the DevOps community are crossing over into the MLOps space yet. On the surface MLOps seems like a natural extension of DevOps, but that is not reflected as a driving factor in MLOps adoption yet.
So Who Will do MLOps?
There are a few different options of who will implement and perform MLOps activities in the future. ML Engineering, also a very new discipline, could perform most of the MLOps functions, with the support of a “traditional” DevOps team. This seems the most likely to me right now based on the proportionally high engagement of data teams in MLOps already. Alternatively MLOps could become a subdiscipline of DevOps, and eventually the most important area of DevOps. DevOps teams would need to acquire new skills to support increasingly sophisticated machine learning products. Another possibility is that we see companies start to hire MLOps Engineers who combine high-level knowledge of ML with expertise in cloud services, tools, and frameworks that are ubiquitous within machine learning. This could mean we will start to see MLOps teams forming, or MLOps engineers as additions to existing data teams.
3rd Party Tools will be Critical
Regardless of what direction the industry moves there will likely be an increase in the number of third party MLOps tools and offerings that will help accelerate software and data teams on their MLOps journey. MLOps adoption will reflect that of DevOps adoption in the early days, but on an accelerated timeline due to more tools becoming available and the maturity of cloud technology and providers relative to the early 2010s when DevOps was new.