Hey, if you’re interested in machine learning, you’ve probably heard that building models is just a small part of the job. In fact, it’s often said that model-building is only about 10% of what ML engineers do. The other 90% is made up of tasks like data cleaning, creating feature pipelines, deployment, monitoring, and maintenance. But is this really true?
As someone who’s starting to learn about ML, it can be a bit misleading. We spend most of our time in school learning about the models themselves, not the surrounding tasks that make them work in the real world. So, how do ML engineers actually get good at the non-model parts of their job? Do they learn it on the job, or is it something you should invest time in to get noticed by potential employers?
I think the key is to find a balance between learning the theory and models, and the practical skills you need to deploy and maintain them. It’s not just about building a great model; it’s about making it work in the real world. This means learning about data preprocessing, how to create efficient pipelines, and how to deploy your models in a way that’s scalable and reliable.
Some ways to get started with the non-model aspects of ML engineering include:
* Learning about data preprocessing and feature engineering
* Practicing with deployment tools like Docker and Kubernetes
* Experimenting with monitoring and maintenance techniques
* Reading about the experiences of other ML engineers and learning from their mistakes
By focusing on these areas, you can set yourself up for success as an ML engineer and make your models a reality.