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MLOps explained: All you need to know about machine learning operations

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There are many different sets of best practices in the IT world that enable you to finish your project quickly and effectively. One of such sets is MLOps – a combination of DevOps and machine learning. What is MLOps all about? And what are the best solutions you should follow in machine learning operations? Let’s take a closer look at these questions and find out if MLOps consulting services are right for you!

DevOps is a direct result of the Agile methodology. The goal is simple – to streamline and shorten the time needed to finish a software-related project. In the DevOps approach, development (Dev) and operations (Ops) teams work closely together. As a result, everything goes smoothly, the end result needs fewer corrections and adjustments, and the product meets all the client’s expectations.

In general, it works pretty much in the same way with MLOps.

MLOps definition

Machine learning operations is a set of good practices that one should follow to finish a machine learning project effectively. As you know from our blog, machine learning models are trained and tested in experimental environments. But when that phase is done, MLOps can be implemented in the project. In this model, data scientists, DevOps teams, and machine learning engineers closely collaborate to move the ML algorithm to production systems. Sometimes, other AI-related specialists are also involved, e.g., data engineers. In other words, it’s all about making your ML project fully operational as quickly as possible. We could say that MLOps enables the application of Agile principles to machine learning projects.

Among many other elements, MLOps comprises:

  • CI/CD (continuous integration and delivery practices)
  • Constant monitoring of the project
  • Testing and validation procedures

MLOps came into existence for a very simple reason. Deploying and productionizing machine learning models is complicated. There are many elements involved, and the whole process consists of up to eight different stages. With MLOps, work is much more straightforward and even less experienced specialists have a transparent mode of action to follow.

On ML-Ops.org, you can find a short infographic perfectly depicting what MLOps is all about:

image source: https://ml-ops.org/content/mlops-principles

What are the benefits of machine learning operations?

The description of this model already says a lot about the benefits behind it, but let’s be more specific. For starters, with MLOps, you can achieve much better efficiency. Every ML project that you work on takes less time and is less prone to errors. This, in turn, also improves scalability. With MLOps, companies working with machine learning can execute more projects at the same time. Finally, because the mentioned products are less prone to error, there is a significant risk reduction. Furthermore, MLOps reduces the so-called technical debt across machine learning models.

Crucial MLOps principles

As we mentioned earlier, every MLOps process comprises three stages:

  1. Designing the ML app
  2. Model development and testing
  3. Deployment and operations

Within these three stages, you ought to follow several important principles. Here are the basic ones:

  • Automation: In the MLOps approach, it is your goal to automate the machine learning model deployment into the software system. This includes ML pipeline automation and CI/CD pipeline automation.
  • Everything has to be continuous: There are four elements that need to happen continuously during the project: Integration, delivery, training, monitoring.
  • Versioning: Every ML model should be tracked with VCS (version control systems) and go through code review.
  • Multiple experiments: MLOps, in its nature, is highly experimental. Before you decide what to put to production, you need to conduct many different experiments and train your model in different ways.
  • Testing and validation: Again, you need to test everything, including data, ML infrastructure, and the correctness of the algorithm.
  • Monitoring: Once the machine learning model is deployed, you need to monitor if it operates as agreed with the client.
  • Implement the test score system: At the end of each project, it is vital to measure its overall performance. ML companies usually use something called ML Test Score. You can read more about it in a paper published by Google. It’s available for free here.
  • Reproducibility: Here, the rule is simple. Every phase of your ML endeavor should produce the same results with the same input.
  • Decrease technical debt with proper documentation and project structure: If you want to lower the amount of additional work or costs, everything should be organized and clearly outlined. This way, the potential future team won’t have to do everything from scratch.

Summary: Machine learning operations

Without a doubt, MLOps is one of the game-changers in the machine learning sector. With this approach, your work is effective and measurable. Plus, you save a lot of time. We strongly encourage every company working with ML to introduce MLOps in their everyday work.

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Christopher Stern
Christopher Stern is a Washington-based reporter. Chris spent many years covering tech policy as a business reporter for renowned publications. He has extensive experience covering Congress, the Federal Communications Commission, and the Federal Trade Commissions. He is a graduate of Middlebury College. Email:[email protected]

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