Improving Machine Learning Models with MLOps

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Improving Machine Learning Models with MLOps

xpresso.ai Team
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MLOps has completely changed the way that machine learning models get developed. It makes creating machine learning solutions easy, and it has made the pipeline more streamlined. MLOps is the short form of the term machine learning operations. It makes deploying machine learning models faster and more economical, as it is a set of practices similar to DevOps in the computer science world. Many people in DevOps understand that creating a pipeline that is fast and reproducible is the best way to write code. DevOps has made developing software easy, and it is doing the same thing with developing machine learning models. We are now in a situation where machine learning engineers and data scientists can collaborate using one system that makes them work faster than ever before. It also creates many MLOps tools we can use for the best effect.

MLOps Brings DevOps to Help Build Machine Learning

Certain problems have been solved by DevOps. For example, the issue of the computing environment has always been a major problem for software engineers. The bugs each developer encounters could change dramatically, depending on the environments they were developing in. DevOps created tools to enable every developer to test on small virtual machines that were environment agnostic. It meant that they could easily see whether their code or environment created a problem. MLOps is bringing the same level of sophistication in the creation of machine learning models. The machine learning pipeline is getting automated as well, and there are many MLOps tools that data scientists have been empowered with. It makes implementing and developing machine learning models a real treat.

Open-Source MLOps Tools

One of the major problems for machine learning has been the availability of certain tools. There is now a massive number of machine learning model development tools available to scientists and engineers. They can clone their environments, which enables machine learning solutions to be created at a breakneck pace. It also means that these developers can all work together with the same available tools. Tools such as version control have been expanded dramatically, and they serve as a foundation of how machine learning model development can work.

Looking at the Machine Learning Pipeline As a Product

One of the many mistakes that machine learning practitioners used to make was to treat the model as a result instead of looking at the process that produces it. The machine learning pipeline is a lot more important than the resulting model. That model will have to be tested and tweaked to ensure that it always produces the results that your stakeholders require. However, if your process isn’t great, you might produce a great model once or twice, but it will never be consistent. To produce models that are great consistently, you have to look at the pipeline and optimize it for the best effect. This is one of the many ways in which MLOps is transforming machine learning model development. It is taking machine learning operations and making them into something more accurate and scientific.

Version Control Of More Than Code

Version control used to be something that was used exclusively for code. However, machine learning solutions require a lot more than just code. You have to keep track of your code, data, and models. Version control for machine learning has to do all of these things, and it has to do them for data scientists and machine learning engineers. It is a necessary software to use throughout the entire process, and it is something for you to consider. By doing these things accurately, the machine learning pipeline gets more streamlined and easier to work with. You produce more accurate models, and it enables many more people to adopt machine learning solutions.

About the Author

xpresso.ai Team
Enterprise AI/ML Application Lifecycle Management Platform