What is MLOps

MLOps, or Machine Learning Operations, is a relatively new term used for a discipline that aims to unify the Dev and Ops parts of Machine Learning through the use of practices that provide scalability, determinism, governance, and agility. It’s modeled after DevOps, a discipline that has existed for around a decade. Many will say that MLOps and DevOps are the same thing – and they wouldn’t be wrong for the most part. However, there are a few key differences that set them apart.

You can think of MLOps as a tool to help manage the Machine Learning Lifecycle, since it solves many of the problems that teams come across.

“MLOps may sound like the name of a shaggy, one-eyed monster, but it’s actually an acronym that spells success in enterprise AI.” – Nvidia

How to get started with MLOps

MLOps platforms are difficult to integrate. However, these problems and challenges can usually be boiled down to the time you need to invest, and the costs of initial implementation. While it may seem a bit costly in the beginning, MLOps can save money and time for organizations in the long term.

Aside from the costs and time needed to integrate MLOps, there are a few key steps and MLOps best practices to mastering the use of it.

  • Translating Objectives into Machine Learning Problems – Development of ML systems usually starts with setting some kind of an objective. These objectives can be practically anything, and they often have KPIs, performance measures, and other requirements.
  • Create Machine Learning and data solutions to the problems – Once we’ve translated objectives into ML problems as mentioned in the first step, the next thing to do is to start searching for input data and models that will be tried for that data and optimization once the model is deployed in the production environment.
  • Preparing and Processing Data – Formatting, feature engineering, cleaning, and setting the features that will contribute to the output of the problem are all parts of data preparation. A complete MLOps pipeline needs to be coded in order to produce clean data that is to be fed into the next step of model development.
  • Data science – When you’ve prepared your data, you can move on to the next phase of training your Machine Learning model. Narrowing down to the best solution by using measures such as precision, accuracy, recall, etc is required for this step, along with the qualitative analysis of the model.
  • Building and automating the ML pipelines
  • Deploying Models – Deploying a machine learning model comes in two options: Static and Dynamic.Dynamic Deployment is when the model is deployed through a web framework that responds to user requests as an API endpoint.Static Deployment is when the ML model is deployed through installable software.
  • Optimizing, maintaining, and ML model Monitoring – This last step should be pretty self-explanatory and expected. Keep an eye on the performance of the deployed models while ensuring fair governance, which in this case means placing measures to ensure that the ML model delivers its responsibilities.

While it might seem like a complicated ordeal, the good news is that it does get easier with time. The more any team uses an MLOps platform, the easier and more valuable it becomes.

Testing. CI/CD. Monitoring.

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Why does MLOps Matter

There are multiple important problems, issues, and bottlenecks that integrating MLOps solves. These are things that teams usually come across, and why MLOps and MLOps tools matter.

  • Shortage of Data Scientists – Good developers who are also able to deploy scalable web apps are hard to come by. This is where new ML Engineers come into play, as a new profile of such engineers aims to help.
  • Ever-changing and ever-evolving business objectives are hard to keep up with. Maintaining the performance of the ML Model and keeping up with continuous model training is likewise hard to keep up with.
  • Communication can end up being the reason that some big projects fail. Not being able to find the common-ground between the technical and business departments due to communication can be a real problem, and as mentioned this has led to some big projects failing in the past. An MLOps platform aims and helps solve this issue.
  • Assessing the risks of an ML system – One of the bigger issues in Machine Learning is assessing the risks of any deployed model deviating from its original task, which is a real issue that happens relatively often.

The above are just a few MLOps use cases where using it instead of just DevOps can help immensely.

What makes MLOps different from DevOps

DevOps and MLOps are similar when it comes to scaling and maintaining APIs, infrastructure, databases and other services for deployed Machine Learning models. There are many out there who will claim that MLOps is the same as DevOps, with the only difference being the newer, more hip name. However, what makes MLOps different from DevOps is how CI and CD work, as well as the fact that MLOps brings the additional continuous testing (CT) process.

MLOps engineers are also expected to have more statistical and analytical capability than a DevOps engineer.


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