This Is What You Need to Know to Build an MLOps End-To-End Architecture

7 principles to quickly bring MLOps to your machine learning projects.

The end-to-end MLOps workflow. Image by Author, inspired by source.

MLOps is a new field that was born due to the high failure rate of most machine learning projects. By failure, we understand the inability to generalize your freshly cooked model and ship it into a production environment to bring value to a specific group of people. Don't get me wrong, there are many excellent machine learning projects/algorithms, but the main issue is that a few have passed the "laboratory" stage of development.

As we all know, machine learning addresses problems that cannot be well specified programmatically. Machine Learning systems are entangled with various dependencies, such as the size of the dataset, the distribution of features within the dataset, data scaling and splitting techniques, the type of optimizer being used, etc.

Using a standardized end-to-end MLOps framework, we can show our incredible models to the world and show people how impressive AI can be.

“Reproducibility is a kindness to your future self and everyone else who might want to build upon your work, which is an excellent goal all on its own.”