The MLOps Engineer: On the Team with Data Scientists to Put AI into Production
MLOPs Engineers work on a team with data scientists and software engineers to oversee the smooth running of AI models in production systems that are strategic to the business.
By John P. Desmond, Editor, AI in Business
The emerging role of MLOps Engineer is to take a model created by a data scientist and work on automating its deployment to the production AI system that needs to make use of it.
MLOps is defined as the discipline of applying DevOps ideas to machine learning systems, so that ML models can be deployed in data science initiatives. A mix of engineers with different titles work together to make successful deployments happen.
The data scientists find and apply the optimum ML model to address the business challenge. In smaller firms, the data scientists can also be the architects and engineers of data, suggests Dairenkon Majime, a data scientist intern at Loft of Sao Paulo, Brazil, a real estate platform, writing on the blog of Neptune.ai. Based in Warsaw, Poland, Neptune.ai provides a metadata store for MLOps.
Software engineers worry about different issues than data scientists, such as access control, use data gathering, cross-platform integration and hosting. Software developers want machine learning models to be available through callable APIs. “Software engineers working on machine learning projects should be data literate, but not all software engineers are model builders,” Majime stated.
Data engineers create data pipelines that provide continuous data flow from sources, also encompassing pre-transformation and storage. This process may be called ETL – for extract, transform and load – techniques used to blend data from multiple sources, such as for building data warehouses, in the days when those were constructed.
How These MLOps Engineers Got Started
MLOps Engineers interviewed by author Majime addressed how they got started in the field, on the role of MLOps Engineers on the team, and any advice they have for those interested in pursuing the field.:
Amy Bachir is a senior MLOps Engineer at Interos, based in Arlington, Va.. The company offers an AI-powered supply chain risk management platform that discovers, visualizes and assesses the supply chain.
“I was a DevOps engineer for a few years before I got into MLOps, so I already had experience in CI/CD (Continuous Integration/Continuous Deployment), GitOps, deployments, monitoring, and automation. However, I was missing the pieces that are unique and specific to the machine learning application lifecycle, so I started looking at online courses to learn the basics. I found a very interesting and comprehensive nanodegree with Udacity for building machine learning models, so I took that nanodegree and graduated from it. After earning that nanodegree and having a solid experience in DevOps, it was easy to get my first role in MLOps.”
Caroline Zago is an MLOps Engeer at XP Inc., an investment management company based in Sao Paulo, Brazil.
“I started as a Data Scientist Intern, but I liked DevOps and Software Engineering. Then, I found MLOps, which brings together the three areas that I like.”
Dmitry Goryunov, an MLOps Engineer at deepset, the company behind the Haystack natural language processing (NLP) framework, based in Berlin, Germany.
“I was lucky to have very understanding data scientists in my first team; together, we saw the MLOps role to ensure that the ML models benefit from the same best practices established in software development. Doing so assures that the ML models in production have more or less the same performance as on the test dataset.”
“The roads to becoming an MLOps Engineer vary a lot. There’s no one recipe. DevOps experts that are interested in machine learning and are already halfway through the process of working with MLOps, are the most typical source of newcomers to the field.”
“No matter how you slice it, being an MLOps Engineer takes hard work. The fact that this is a new area is one of the biggest challenges. As a result, in many situations, there may be a lack of available material. However, a number of tools are available to help you become more specialized and professional. Deeplearning.ai’s Machine Learning for Production (MLOps) specialization on Coursera is one of the most well-known, as are a number of materials from O’Reilly.”
On the difference between MLEngineers and MLOps Engineers:
Alexey Grigorev, principal data scientist at OLX Group, based in Amsterdam, offering an online trading platform, stated, “For me, it is the same as the difference between engineering and Ops teams. Engineers create software, Ops provide the infrastructure for running it and make sure the software runs reliably. However, the lines are blurry, and MLOps Engineers can (and often should) do things end-to-end.”
On the relationship between the MLOps team with the data science team at your company:
Bachir: “It is a very close relationship. We work together all the time. For the most part, everything we build is for them, so we consult with them before we design anything. We also run quarterly surveys to get their feedback on existing systems and what they think might be missing or is not working well for them. In general, I think it is very important to loop the end-users in everything we build because they are ultimately the consumers, and what we build should solve their problems and fit their needs.”
Advice for anyone thinking about a career in MLEngineering:
Bachir: “This is a tricky one! MLOps is in a very exciting role! There is much room for innovation! You will never get bored. At the same time, it is a very challenging role. The combination of skills required to succeed at this role is almost impossible to have in one person, so prepare yourself for much learning.”
Goryunov: “It depends on the background the person has. If somebody comes from a software engineer job family, as I do, the advice would be to learn at least the basics of machine learning.
I am not speaking about getting a PhD in the area, more like going through an online course or reading a couple of books. It is easy to find much material online these days. Courses from Andrew Ng are how I got started.“
“It will not be easy to wrap your head around ML concepts and get a basic understanding. At least, it was not easy for me. But, I assure you, it pays off. The basic understanding of ML lets you speak the same language as your data scientists, which is important for ML projects’ success.”
“While studying ML, you might find out that one of the topics is more interesting than the others. If this is the case, go and read a few papers or watch a few videos explaining them. Videos work even better sometimes. Go deeper into a specific topic. It is more fun to learn about ML that way.”
Confirms Differing Roles of Data Scientists and MLEngineers
Confirmation of the differing roles of data scientist and MLEngineers came from Henrik Skogstrom, Head of Growth at Valohai, an MLOps platform headquartered in Finland, from an account he authored on the blog of Valohai.
“A data scientist typically does the statistical analysis needed to understand which machine learning approach needs to be used during a project. They’ll then create a model of that algorithm and prototype it. They’re specialists in understanding the predictive models and underlying mathematics behind machine learning,” Skogstrom stated..
“On the other hand, an ML engineer will take the data scientist’s prototype model and ensure that it will work in a production environment. Adhering to sound engineering principles, they’ll also ensure that models can be reproduced and redeployed and that everything is versioned correctly. Simply put, they are responsible for the productization of machine learning models.
While data scientists often have in-depth specialist knowledge, ML engineers are expected to have a more cross-functional understanding of engineering and machine learning.”
(Write to the editor here.)
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