The Future of MLOps: Challenges, Solutions and Trends

MLOps are one of the big names in AI these days. As with all new trends, they bring excitement and speculation along with them. But what exactly is this development, and where is it headed?

Many aspects of technology can be confusing for most of us. However, if broken down into plain language, trying to make sense of technological developments can be a lot less arduous. Let’s take a look at the facts surrounding MLOps, where they come from, and where they seem to be going.

What is ML, and What are MLOps?

1. Machine Learning

MLOps stands for machine learning operations, which are an essential part of machine learning. Machine learning is the branch of artificial intelligence (AI) that utilizes algorithms to manipulate data in order to imitate human behavior. Machine learning is used for many different practical applications, including speech recognition devices, imaging techniques used in medicine, and other forms of medical diagnosis.

More generally, machine learning is being developed for even more forward-thinking processes. For example, it is used for the purposes of “extraction,” meaning that it can gather and analyze data from multiple data sets to generate models for disease prevention before they occur. It can also be used in finance, for example, to detect fraudulent financial activity in much more sophisticated ways than traditional methods can.

2. MLOps

MLOps are, quite simply, a way of streamlining machine learning. MLOps involve the use of continuous integration and deployment (CI/CD) mechanisms that allow practitioners to work more closely, more efficiently, and more effectively than they did previously. They started coming into usage in 2018.

CI/CD are software development practices that allow code changes to be made faster and more reliably than they used to be. In short, it automates the process of software delivery. CI/CD practices build codes to reduce errors and increase the speed of operations in software usage.

Utilizing these techniques is very beneficial in the machine learning development process as the field is extremely complex, and each component taken separately involves a great deal of time, as well as error risk.

3. Sequence of MLOps

In working with ML data, MLOps undergo the following phases:

• Exploratory Data Analysis (EDA)
• Data gathering into models
• Model testing
• Model test result analysis
• Reapplication of models

Challenges Being Faced by MLOps

Given that these operations are still relatively new in the AI world, there are naturally challenges that are being faced by users. This applies both to the companies employing the methods to analyze their data, as well as the scientists performing the analysis.

1. Challenges of Working with Client Companies

There are different categories of challenges that MLOps present. Perhaps the most general one has to do with the client companies who hire tech experts to carry out analyses.

Because of the complexity of MLOps, it is very difficult to explain how they function to non-experts. Therefore, those wishing to receive analysis of their data often expect too much in terms of precision, forecasting, and other aspects of data analysis. It is hard to understand just how great the margin for error is and what realistic expectations should be for accuracy in results.

Solutions

As the field develops, machine learning should develop more commonly-understood jargon, simpler models for laypeople to understand, and more distinct standards for what individuals and companies employing the techniques involved should realistically hope to achieve.

2. Challenges of Working with Data

MLOps are still somewhat in their nascency as far as data processing is concerned. Although the science’s techniques are potentially superior to those of machine learning by itself, it has not yet gotten to the point where the different types and dynamics of data that go into its functioning can really be accurately sorted efficiently and without error.

A lot of this problem comes down to the issue of “versioning,” or the tracking of a given data set as it changes. Sometimes data that changes as it develops causes errors in previous versions that can then no longer be accurately analyzed by the MLOps.

Solutions

Data versioning can be optimized by means of keeping up-to-date on applicable codes, keeping track of shared repositories, and maintaining traceability of data. Although these operations can be extremely complex, there are tools that can assist analysts carry them out.

3. Challenges of Working with Models

A similar challenge to working with data is the challenge of working with models. Model versions too are subject to error and therefore not fully reliable. These issues ultimately come down to the data involved as model data can not be configured correctly or scaled in the right way to fit a given model. There are also issues of attempting to use single models both in clouds and hardware where they might not be compatible.

Solutions

Models need to be refined to the point where they cannot accept data that isn’t suitable. If a given model isn’t compatible between the virtual and physical spheres, it should be limited in its usage.

New Trends among MLOps

Because of the increasing attention people are paying to issues in MLOps, there are already emerging trends in addressing these issues. 2022 saw a rise in the following trends:

Increased Attention to Data Drift

Data drift is the term used for data that gets lost from a given model and therefore throws the algorithms being used out of whack. Tools to detect and catch data drift are being refined and will see an even further improvement in the near future.

MLOps for Everyone

As mentioned above, one of the biggest issues with MLOps is that relatively few non-experts know what they are. This year has seen an increase in mainstreaming the concept and the simplifying of terms for general audiences.