The ML Model Deployment Process and Problems Doing It

The-ML-deployment-process-and-problems-doing-it Blogs

The ML Model Deployment Process and Problems Doing It Team
Share this post

Machine learning is difficult because the deployment process takes up the majority of developer time. According to a famous paper called Hidden Technical Debt in Machine Learning Systems, it takes longer for companies to deploy a machine learning model than to develop it. It makes the deployment stage crucial to automate, like what has happened in traditional software engineering. However, many challenges prevent companies from automating that process so smoothly. One of those challenges is that machine learning usually involves two sets of people who don’t use the same tools. The data scientist responsible for developing the model uses Juypter notebooks to develop and create their models, but they usually pass it off to the machine learning engineers that use Python or Java as a way of deploying models into production. The pipeline to go from data scientist to machine learning engineer is one of the most important processes that need to be automated.

The Traditional Way of Deploying Software

Traditional software development is a lot easier than developing an ML model because it only involves code most of the time. Certain programs will require you to integrate graphics for icons and other imagery, but most software development involves taking code from the development environment to a finished binary. While you do perform static and unit tests, those tests can be performed and automated with other programs. It makes DevOps a much more straightforward job than MLOps. Software engineering is also a much older field, and there are many mature processes already in place. The code that is already in place has been built up over many decades. There are dedicated researchers from massive institutions finding answers to any new challenges that come up. For example, when a distributed system needed to be built, companies like Google could come up with their own solutions for this task. The main challenge of deploying a machine learning model is the fact that this level of automation has not been achieved yet. On top of that, machine learning is dependent on data to develop the models, and you still need to keep an accurate catalog of that data through some version control system.

Deploying Machine Learning Projects

What makes machine learning projects so difficult is that it is a lot longer than the software engineering process. You need to start with data management, which means you have to find the data you will build the model from. That process usually involves finding, analyzing, and fixing the data you will need. The data usually doesn’t come in a state that can be analyzed. You have to spend a lot of time preparing the data to be used in your model. You also have no way of testing whether this data was fixed properly or not, so you eventually have to come back to this step many times before deploying a model into production.

 After the data preparation stage, you are now ready to start developing your model. You will then develop algorithms and statistical processes to train the data and also do validation to ensure that this training was accurate. You will know that your model is trained properly and your data is correct when you can accurately predict the future or get a correct answer with whatever problem you are trying to solve. Finally, after all of that has been completed, you are now ready to build a working application that incorporates your fully trained model.


As you can see, the process is a lot more difficult than software engineering and DevOps. It is why ML’s tools like xpresso are so crucial to the future of this industry.

About the Author Team
Enterprise AI/ML Application Lifecycle Management Platform

Leave a Reply