Most Common Problems with MLOps

The Most Common Problems with MLOps and How to Address Them

MLops is a big name in tech these days. Machine learning operations are the component of machine learning that attempts to streamline and improve this critical function of artificial intelligence. Since machine learning came onto the scene several years ago, there has been a great deal of discussion about how its operations should be conducted and which specific programs should be utilized. MLOps attempts to address operative questions by working with coding in software to improve its performance.

As promising as they are for advancements in many different types of applications, MLOps are not without flaws. Still being relatively nascent in their development, there are a number of aspects of MLOps that still need to be ironed out before they can be systematized and used more widely.

In general, there are two different categories of problems with MLOps: those stemming strictly from human error, and those that are a combination of inherent problems within the science itself and incorrect utilization by people. This article will summarize the basic features of these two categories of problems.

Problems Stemming from Human Error

Inability to Understand and Utilize Correct Models

The most general cause of problems in MLOps is the simple fact that the people using them do not fully understand how they work. In practice what this means is that the models employed must be followed throughout the course of a given operation in order for it to be fully successful. If the person performing the operation does not know what coding to look for or how certain algorithms work, the data might fall out of line with the model and the end result will be faulty. 

In short, there is not yet sufficient automation within MLOps analytics to compensate for this kind of human error. Because individuals are often given the job of handling models, they are not adequately educated on how to follow the models throughout the course of a given operation. There are tools that can be utilized to guide users in MLOps modeling, though, and anyone who thinks they might need help should look into them.

Incorrect Application of ML

A related problem is the incorrect application of machine learning to areas where it shouldn’t be applied. It may seem given that a model is a model and that each one should be appropriately labeled and applied. But this is often not the case in reality, and data ends up getting misplaced.

One example of this is in the use of big data. The size requirements of ML architectures for accurate functioning can sometimes be quite large. One might be tempted to think that simply putting the “right” data into the system will result in accurate analyses. However, in some areas the “empty spaces” left by insufficient amounts of data need to be filled in order for operations to be carried out smoothly. Otherwise, the data that is used ends up floating to some extent and runs the risk of getting lost.

Problems Manipulating Coding

Also, as in any science, data types need to be of the right sort in order to produce accurate results. If you are working on a speech recognition program, for example, and only include data from limited demographic groups, your resulting model should not be generalized to include the entire population because it likely won’t be applicable to many groups.

The issues also come down to the most specific aspects of operations. Coding, for example, is something that needs to be monitored closely, and humans are often responsible for updating endpoints in models as they get passed along from version to version. This also leaves open the possibility for human error, which will inevitably occur. Having operations automated for endpoints will eliminate this problem.

Problems in the Larger System

Incompatible Programs

The problem of incompatible programs is one that involves both systemic problems and the risk of human error. This issue deserves attention as the inappropriate crossing of models is the cause of a lot of inaccurate analysis. Programs are developed individually, each using their own coding and language. Therefore, each program should be handled separately. Perhaps there will come a day when greater inter-usage of programs becomes standardized. If it does, the programs in question should be labeled appropriately.

Models Running Ahead

Another problem is that models are growing at a greater rate than they can be monitored. What this means in practice is that tech companies are not willing to fill the number of positions necessary to keep track of the changes. While the system as a whole could be refined and made more efficient if there were greater numbers of people monitoring operations, the current situation is a vicious cycle of data spinning further and further out of  control from its users. Employers would be wise to hunker down and invest early in monitoring the progress so that operations can later be maintained more easily.

Lack of Integration Among Players the System

Related to this, people who run into and actually notice code-related problems are often without support when it comes to addressing these problems. Keeping operations going is more than a matter of individuals sitting before their computers conducting analyses—it’s about programs and participants working together on different ends of operations to keep things running smoothly. Those on the back end of the operations need to keep their eye on the ball, as well.

Test, Support, and Improve

All of the above-mentioned problems can be targeted by means of a focused effort on the part of companies and individuals creating and distributing machine learning systems. If people take the time to focus on data-related issues, give sufficient time and attention to those analyzing operations on a daily basis, and make an effort to record and classify new information systematically, MLOps will be much improved. 

When this happens, the whole methodology will become streamlined, more efficient, and more widely applicable as experts will know which aspects to tweak to make diverse systems more compatible. Improvements don’t have to be very far off if they are given sufficient attention. And when the whole field starts to move ahead, new advancements and applications will surely follow.

Also Read: Top 11 Regression Testing Tools

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