We all know some job professions that are commonly claimed to be performed only by a specific group of people – naturally equipped with particular characteristics. Let’s take surgeons: above all, they are expected to be extremely precise.
Maybe besides orthopedists, with all their hammers, drills and saws – no offence. How about psychologists? In order to understand their patients, they need to be very empathetic and analytical. Are there any super powers that an MLOps should have then?
- What is MLOps?
- MLOps – going beyond PoCs
- The art of anticipation
- MLOps & DevOps
It might be wise to take a step back and define what an MLOps is before delving further into the subject. Google Cloud Docs after:
By monitoring and automating each phase of the solution, MLOps aims to manage the whole lifetime of ML solutions in production. Additionally, we don’t simply mean the ML model when we say “all steps.” the context of reality, a machine learning (ML) system is made up of many various components, such as data collection, data verification, model analysis, debugging and debugging, serving infrastructure, monitoring, and many more.
Going beyond PoCs with MLOps
Few individuals specialize in MLOps because it is still a relatively new idea. Currently a niche market, it is predicted to expand considerably more swiftly than anticipated to meet the enormous demand for commercial applications. Businesses who are already creating ML solutions will ultimately need to progress beyond the Proof-of-Concept stage and towards productionizing their ML solutions in the era of ubiquitous digital transformation, particularly in the post-covid world. However, before all of the excitement materializes, MLOps may simply be a great niche being filled by hipsters from the IT industry. Sounds posh, doesn’t it?
A technique for anticipating
It is commonly known that mathematicians approach issues with rigor. For instance, physicists are frequently content with MVPs like “rule works for n=3 dimensional space,” but mathematicians would first verify to see if the rule still holds true if they choose any nN. In order to demonstrate that the solution is effective, they will attempt to confirm as many presumptions as they can. At least, it is what they have long been instructed to do.
“Just it makes some sense, but how dare we demand MLOps to be pessimists?” you may ask. A well-produced ML system should be susceptible to undesirable circumstances and be able to handle them effectively. Pessimists are the best at predicting the worst-case outcomes, after all. But this should be a certain kind of pessimism. That kind I would categorize as “active pessimists.” In contrast to a “passive pessimist,” a “active pessimist” not only foresees potential issues but actively works to develop viable solutions. This innate ability to anticipate potential problems may be very helpful in developing reliable ML systems.
DevOps and MLOps
Not to mention, many math grads go on to work with data as data engineers, data analysts, data scientists, or machine learning engineers. That shouldn’t come as a surprise given that they are thought to be the group that is exposed to computations and numbers the most. The more familiarity they have with various data solution phases, the better.
The deployment step, however, is rarely touched, according to practice. When productionized, ML solutions are frequently at MLOps Level 0, which is essentially a manual process.
The hitch is that DevOps is a large field that must be researched first if one wishes to become an MLOps. As a result, I feel that experience dealing with data at various stages with all of the aforementioned characteristics makes such a DevOps expert an even better MLOps engineer.
Have you had enough of modifying your models’ hyperparameters? Perhaps it’s time to go deeper into DevOps. Your top abilities and inborn characteristics may be ideal for this achievement.