You have probably heard of the term DevOps already, as well as machine learning and artificial intelligence. DevOps was first introduced a decade ago as a practice that seeks to unite developers and IT operations to collaborate better. However, as the volume of data became more and more unmanageable for normal human capacity, the use of artificial intelligence and machine learning, therefore, became a more pressing necessity. After all, we live in the golden age of digitization and technology.
This is where MLOps came into existence.
What Is MLOps?
One can say that MLOps is just a level higher than DevOps. It is its expected evolution. In a nutshell, MLOps is similar to DevOps in that it is a practice that involves software developers and IT operations teams with the addition of data scientists and ML and AI experts. They seek to find useful applications of the various technologies available, particularly in creating models and ensuring their efficiency.
Why Do Companies Need to Consider MLOps?
While machine learning is a valued addition to any company’s tech arsenal, the actual application, and much more, its productization still proves to be a challenge. After all, the production process with machine learning involves multiple components from data collection, data prep and analysis, model creation, model review, and more. Thus, it will require collaboration among various teams to make it possible.
The Benefits of MLOps
There are different advantages to having MLOps. Here are three:
The first reason why you should consider MLOps is the efficiency that it can bring to your company. MLOps allows data scientists to focus on their tasks more and achieve the faster development and deployment of their machine learning models.
Developing an ML product can require thousands of different models to be overseen, managed, and monitored. Fortunately, MLOps can help stay on top of all of them, especially with the help of other teams.
Machine learning is great, but it is not without faults. One recurring issue is called drifting. This happens when there is a significant shift in the data and its sources.
For instance, suppose that you have developed your ML model to target a specific target demographic known for their activity on a particular platform, let’s say Instagram. Then, suddenly, a newer platform comes along, and they move there (such as Tiktok). This will thus render your data inaccurate and inefficient. This can be easily overlooked to add more to the challenge, especially since such shifts are not always dramatic and significant. Instead, some of them can gradually “drift”. Fortunately, with more teams involved in monitoring your ML models, such risks can be abated a lot easier. Uploading your machine learning to the cloud can also allow you access to other digital tools that can keep your data teams up to date and follow the best practices.
The MLOps Platform
Speaking of digital tools, we also recommend looking into the use of MLOps platforms. An MLOps platform is essentially a digital, collaborative environment where your teams can meet, monitor, and work on various models simultaneously.
Since it is a relatively new process, MLOps platforms are still not as developed and widespread as we wish them to be. The good news is that industry innovators are constantly developing and refining their programs to make them better each day.
With the demand, it is also not a surprise that there are already several choices in the market. Not all platforms are created alike. Hence, if you want to find the one that best suits your needs and preferences, it is important to determine the features that set them apart.
Do they have their own cloud, or will the platform rely on a public one? Does it cater exclusively to machine learning alone, or will it allow you to work on deep learning as well? How user-friendly is it for users that have limited machine learning knowledge? More importantly, how can it be accessed by multiple users and still keep it secure?
The Future of MLOps
With its unlimited applications, the future is indeed bright for machine learning. This means that we can expect MLOps to undergo many changes in the following years. Regardless, getting a headstart and forming your own MLOps team as soon as possible will help you stay ahead of your competitors.
Having ML as common ground, we are confident that your teams will work together better than against each other. At the very least, it can make your operations smoother and more seamless, especially when multiple teams are involved. After all, though it is pretty unfortunate, not all companies have teams with stellar relationships with each other.